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Research on Grey Incidence Measurement Method Based on dynamic time warping Distance

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Research on Grey Incidence Measurement Method Based on Dynam...

Within-day travel speed pattern unsupervised classification——A data driven case study of the State of Alabama during the COVID-19 pandemic

第25届全国灰色系统会议

作者： Jin Dai Xin Liu Feng Hu College of Software Engineering Chongqing University of Posts and Telecommunications

Grey incidence measurement method is core content of grey analysis. As the traditional grey incidence measurement methods only handle sequences with same length, the sequences with different length must be padded by deleting data, mean statistic method or GM（1,1） model prediction. It results in the degree of grey is continued to enlarge and loss useful information. On the basis of dynamic time warping distance, a novel grey incidence measurement method is proposed. It measures the degree of grey incidence by the shortest path in distance matrix of sequences and does not need to pad any data of sequences. Meanwhile, the corresponding grey incidence measurement method to sequences with different length based on dynamic time warping is constructed. It is effective solutions to solve the problems, such as the incidence measurement between grey sequences with different length, timeline stretching and bending. Simulation results show that the method obtains the correct quantitative analysis conclusion when Deng-Degree and generalized degree are failed.

关键词： grey theory grey incidence analysis degree of grey incidence dynamic time warping

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Journal of Traffic and Transportation Engineering(English Edition) 2021年第2期8卷 170-185页

作者： Niloufar Shirani-bidabadi Rui Ma Michael Anderson Connecticut Transportation Institute University of ConnecticutStorrs-MansfieldCT 06269USA Department of Civil and Environmental Engineering University of Alabama in HuntsvilleHuntsuilleAL 35899USA

Recent comparative studies on mobility patterns are emerging to describe the changes in mobility patterns due to the COVID-19 pandemic.Most of the current studies utilize travel volume per day as the critical indicator and identify the impacted period by the dates of governmental lockdown or stay-at-home orders,which however may not accurately present the actual impacted dates.The objective of this study is to provide an alternative perspective to identify the normal and pandemic-influenced daily traffic patterns.Instead of only using traffic volumes per day or assuming the impacted travel pattern began with the stay-at-home order,the methodology in this study investigates the within-day timedependent travel speed as time series,and then applies dynamic time warping algorithm and hierarchical clustering unsupervised classification methods to classify days into various groups without assuming a start date for any group.Using the state-wide travel speed data in Alabama,these study measures dissimilarities among within-day travel speed time series.By incorporating the dissimilarities/distance matrix,various agglomerative hierarchical clustering(AHC)methods(average,complete,Ward’s)are tested to conduct proper unsupervised classification.The Ward’s AHC classification results show that within-day travel speed pattern in Alabama shifted more than two weeks before the issuance of the State stay-at-home order.The results further show that a new travel speed pattern appears at the end of stay-at-home order,which is different from either the normal pattern before the pandemic or the initial pandemic-influenced pattern,which leads to a conclusion that a’new normal’within-day travel pattern emerges.

关键词： COVID-19 Within-day traffic dynamics dynamic time warping Hierarchical clustering Unsupervised classification

Cross-Band Spectrum Prediction Based on Deep Transfer Learning

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China Communications 2020年第2期17卷 66-80页

作者： Fandi Lin Jin Chen Jiachen Sun Guoru Ding Ling Yu College of Communications Engineering Army Engineering University

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.

关键词： cross-band spectrum prediction deep transfer learning long short-term memory dynamic time warping transfer component analysis

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

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International Journal of Intelligent Computing and Cybernetics 2018年第4期11卷 486-495页

作者： Ke Yi Zhou Shaolin Hu School of Automation and Information Engineering Xi’an University of TechnologyXi’anChina School of Automation Foshan UniversityFoshanChina

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 themorphologica

关键词： dynamic time warping Morphological representation Similarity measurement time series data

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

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Green Energy and Intelligent Transportation 2022年第1期1卷 121-132页

作者： Shuowei Li Caiping Zhang Jingcai Du Xinwei Cong Linjing Zhang Yan Jiang Leyi Wang National Active Distribution Network Technology Research Center(NANTEC) Beijing Jiaotong UniversityBeijing 100044China Sunwoda Electronic Co. Ltd.Shenzhen518100China Department of Electrical&Computer Engineering Wayne State UniversityDetroitMI 48202USA

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.

关键词： Electric vehicle Fault diagnosis Extended average voltage dynamic time warping Feature clustering

Aerial battlefield targets grouping based on DTW-DBSCAN algorithm

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Aerial battlefield targets grouping based on DTW-DBSCAN algo...

Research on Nearest Neighbor Classifying Method in time Series Based on KPCA-CDTW

第40届中国控制会议

作者： Shao Zhuang Chen Chen School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System

Targets grouping is the basis of second-level information fusion,which can effectively assist commanders to make decisions.Traditional aerial targets grouping algorithm only takes the radar acquisition data at the current time as the object and cannot update the clustering results automatically.A grouping method combining dynamic time warping（DTW） and the algorithm Density-Based Spatial Clustering of Applications with Noise（DBSCAN） is proposed.The DTW distance of each attribute historical time-series data is used to measure the similarity between targets.Besides,an improved DBSCAN algorithm is used for clustering.The simulation results show that the method has better grouping effect and can automatically cluster regularly.

关键词： targets grouping dynamic time warping density clustering

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Research on Nearest Neighbor Classifying Method in Time Seri...

Research on Dance Evaluation Technology Based on Human Posture Recognition

第30届中国控制与决策会议

作者： Yuxiang LIU Meiying QIAO School of Electrical Engineering and Automation Henan Polytechnic University Collaborative Innovation Center of Coal Work Safety

Conventional dynamic time warping（DTW） warps the time axis（X-axis） only by considering the numerical similarity of time series, ignoring the offset on the numerical axis（Y-axis） and being sensitive to singular values, so the similarity of two time series cannot be accurately measured. Firstly, to solve "singularity" problem, this paper improves the local distance of traditional DTW, the new method is called CDTW（Combination dynamic time warping）, which combines the derivative feature with numerical feature of time series in the form of summation, the numerical feature K can be found by simple adjustment. Secondly, we integrate CDTW and KPCA（Kernel Principal Component Analysis） as KPCA-CDTW to reduce the dimensionality and the time complexity. Finally, we classify the time series by the nearest neighbor（1-NN） classification algorithm to test the validity of KPCA-CDTW, the experimental results on Coffee, Strawberry and other four datasets show that the new method has higher classification accuracy and much less classification time than other algorithms, which proves the effectiveness of this method.

关键词： dynamic time warping time series Kernel principal component analysis Classification

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国际计算机前沿大会会议论文集 2023年第1期 78-88页

作者： Yanzi Li Yiwen Zhu Yanqing Wang Yiming Gao Nanjing Xiaozhuang University Nanjing 211171JiangsuChina

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.

关键词： Body Pose Recognition Pose Estimation dynamic time warping Deep Learning

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

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Journal of Computer Science Research 2023年第3期5卷 15-21页

作者： Erdem Tuncer Faculty of Technology Biomedical Eng.Department of Kocaeli UniversityKocaeli41001Turkey

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.

关键词： ECG Sleep apnea Classification dynamic time warping Median frequency