Together with a weighting function that considers the local strength of the inspected ultrasonic signals and a modified partial array reference correction , dynamic time warping proved to be a robust estimation scheme for phase aberration . This is demonstrated for aberrated synthetic and experimental data in a comparative evaluation of dynamic time warping together with well-known aberration correction techniques like cross-correlation and speckle look-back . This paper proposes an enhanced freeway incident detection algorithm that is transferable to different freeway segments . The algorithm is a modified form of previously proposed dynamic time warping-based algorithm that utilizes the concept of pattern similarity amongst microscopic traffic variables time series while using only minimum prior knowledge . Unlike previous approaches where empirical incident precursor patterns are directly used for calibration , the proposed method uses artificial incident precursor patterns associated with probabilistic models derived from hierarchy clustering of incident precursor patterns database . This step gives a new template of M feature vectors , where M<<N. The second step takes into account the frequency discrimination features which discriminate each word of the vocabulary from the others or a set of them . An important characteristic of this process is that the new templates do not need time alignment with the references in the comparison step , avoiding the use of the dynamic time-warping process . The speech recognition results show a significant improvement in the recognition performance with a digit database and the confusable E-set.<<ETX > > The proposed method is time efficient as it is based on histograms of action poses , extracted from MoCap data , that are computed according to Hausdorff distance . The histograms are then compared with the Bhattacharyya distance and warped by a dynamic time warping process to achieve their optimal alignment . This process , implemented by our dynamic programming-based solution , has the advantage of allowing some stretching flexibility to accommodate for possible action length changes . This study presents preliminary results on the use of dynamic time warping to distinguish between the effects of vowel identity and postvocalic consonant voicing on the formant trajectories of English front vowels . Using PraatR ( Albin 2014 ) , formant trajectories are extracted from sound files in Praat and imported into R , where the dynamic time warping analysis is conducted using the dtw package ( Giorgino 2009 ) . Albin , A. L. ( 2014 ) . Kinematic variables were computed from the position , speed and acceleration time series . Two-way ANOVA , Passing – Bablok regressions , and dynamic time warping analysis showed good to excellent agreement between Elite and DYSKIMOT , both at the qualitative level of the time series shape and at the quantitative level of peculiar kinematical events ’ measured values . In conclusion , DYSKIMOT sensor is as relevant as a gold-standard system to assess kinematical features during fast head rotations in participants and patients , demonstrating its usefulness in both clinical practice and research environments . Histogram Equalization is used for contrast stretching , Gabor wavelets and Kernel 2D PCA is used to extract local and nonlinear features and those are invariant towards orientation & spatial locality . Those features are fused with PCA fusion and then Two Dimensional Dynamic Time Warping is used for the classification of those feature vectors . The constraints Continuity , Monotonicity and bounded properties of DTW will identify the non linear optimal path between two dimensions of feature vectors simultaneously . Battery capacity estimation is a significant recent challenge given the complex physical and chemical processes that occur within batteries and the restrictions on the accessibility of capacity degradation data . In this study , we describe an approach called dynamic spatial time warping , which is used to determine the similarities of two arbitrary curves . Unlike classical dynamic time warping methods , this approach can maintain the invariance of curve similarity to the rotations and translations of curves , which is vital in curve similarity search . Second , a spectral time-frequency analysis segmentation approach is employed to generalize all potential time-variant lane-change and lane-keeping candidates . The final stage compares two possible classification methods—1 ) dynamic time warping feature with k -nearest neighbor classifier and 2 ) hidden state sequence prediction with a combined hidden Markov model . The overall optimal classification accuracy can be obtained at 80.36 % for lane-change-left and 83.22 % for lane-change-right . The authors are researching on the new cognitive capability enabling robots to judge about the correctness of the rehabilitative exercise performed by patients following the robot 's indications . The proposed method uses the Dynamic Time Warping functionality comparing the correct sequence ( encoded in the Knowledge Base ) with the human actions being observed by the robot 's eyes . The proposed approach is an enabler of better sustainable rehabilitative care services in remote residential settings because of lowering the need of human care . In this paper a new approach based on Gaussian mixture hidden Markov models ( GHMM ) is introduced , evaluated and compared with traditional approach for isolated word recognition system . A new parameter is introduced in the HMM as defined as the prototype word represented the class word , is calculated in the training phase by the iterative algorithm based the dynamic warping time technique ( DTW ) . The full and diagonal covariance matrix is used for word recognition . Early applications of neural nets to speech recognition used Time - Delay Neural Nets which can be seen as simple forms of CNNs without pooling or subsampling . Full - fledged CNNs with pooling and subsampling were soon applied to speech recognition and combined with dynamic time warping . While the globally - trained combination of neural nets and HMMs for speech and handwriting goes back to the 1990s , only due to recent developments HMM / DNN hybrid modeling became dominant in ASR . Time Series Analysis is a statistical methodology for analyzing time series data , which are data points measured over time , in order to extract data characteristics and meaningful information . Time Series Analysis is an important subject because time series data are generated in many disciplines , e.g. , economics , engineering , health science , meteorology , etc . It is important to learn the different overlapping patterns in the long term and short term time series data . Conventional methods are based on analysing time series data using only one time series analysis technique . The principal aim of this Thesis is to examine the concurrent overlapping features of time-series data using an improved version of three important time-series analysis techniques such as Locally Weighted Scatterplot Smoothing ( LOWESS and the related algorithm LOESS ) , change point , trend , and control chart patterns . Time-series data analysis is essential in many modern applications , such as financial markets , sensor networks , and data centers , and correlation discovery is a core technique for the analysis . In this paper , we address a novel problem that computes a k-sized time-series dataset where the minimum Pearson correlation of any two time-series in the set is maximized . An initial evaluation with SensorScope , an outdoor temperature dataset , confirms that our solution is able to detect and classify faulty readings into four fault types , namely , 1 ) random , 2 ) mal-function , 3 ) bias , and 4 ) drift with accuracy up to 95 % . The results also show that , with the experimental dataset , the time series data analysis technique performs comparable well in most of the cases , whilst in some other cases the support from neighbourhood voting technique and histogram analysis helps our hybrid solution to successfully detects the faults of all types . We propose a distributed medium access control protocol for cognitive radio networks to opportunistically utilize multiple channels . Under the proposed protocol , cognitive radio nodes forecast and rank channel availability observing primary users ’ activities on the channels for a period of time by time series analyzing using smoothing models for seasonal data by Winters ’ method . The proposed approach protects primary users , mitigates channel access delay , and increases network performance . However , they also pose a disadvantage as successful insertion into the stratum corneum layer of the skin relies on sufficient mechanical strength of the biodegradable material . This review looks at the various technologies developed in MN research and shows the rapidly growing numbers of research papers and patent publications since the first invention of MNs ( using time series statistical analysis ) . This provides the research and industry communities a valuable synopsis of the trends and progress being made in this field . Hotspot can also be defined as an overloaded condition of the Physical Machine . Our paperaims at resolving the issues of the imbalance of resource utilization using “ A Time Series based Analysis Technique ” . It is used for the effective utilization of resources and to retain the downtime during the detection of Hotspots in case of occurrence of any sudden spikes . Forecasting model of coal rock electromagnetic radiation was built with combining time series analyze and adaptive neuro-fuzzy inference system ( ANFIS ) . In the first , coal rock electromagnetic radiation phase space was reconstructed through Takens theory , and time delay and embedding dimension are determined by mutual information method and false nearest neighbor method respectively . A short summary of the world-system perspective on international relations suggests a number of hypotheses regarding processes that operate at the level of the whole system . The feasibility of testing these propositions with the time-series method of analysis of a single case is discussed , and problems arising from the use of this research design are considered . There are eight anticipated problems regarding operationalization and measurement : ( 1 ) bounding and mapping the system and its constituent zones ; ( 2 ) validity and reliability of measures over time ; ( 3 ) limitations of aggregating data on nation-states to create contextual world-system variables ; ( 4 ) transformation of nation-state data to make it more suitable for world-system research ; ( 5 ) combining data from different sources in the same indicator ; ( 6 ) noncontinuous data series ; ( 7 ) the width of a time point and measurement error in time ; and ( 8) the small number of instances of long-run processes over the 500-year history of the world-system . These operations are efficient to cope with broken and touching characters in words . The distortion of character shapes is handled by coupling the string matching process with local shape comparisons that are achieved by Dynamic Time Warping ( DTW ) . The costs of the SDE operations are provided by the DTW distances . To produce reliable matching results even though there are large translation and rotation of a sensor , we proposed a method that combines the advantages of sum of squared difference ( SSD ) and dynamic time warping ( DTW ) . Dominant corresponding feature pairs are found using a proximity matrix and an SSD-based similarity matrix , and then the remaining feature matching is accomplished by DTW . Experimental results show that a zero failure rate of matching can be achieved in an indoor environment if the baseline is less than 20 cm . To address the accuracy and real-time performance problem of human identification , a fast and robust ECG-based identification algorithm was proposed in this study , which was particularly suitable for miniaturized embedded platforms . Firstly , a dynamic-threshold method was used to extract stable ECG waveforms as template samples and test samples ; then , based on a modified Dynamic Time Warping ( DTW ) method , the degree of difference between matching samples was calculated to reach a result of recognition . Considering that ECG is a kind of timevarying and non-stationary signals , ECG template database should be dynamically updated to ensure the consistency of the template and body status and further improve recognition accuracy and robustness . The Global Data on Events , Location and Tone ” ( GDELT ) to model the image of China in mass media , specifically , how China has related to the rest of the world and how this connection has evolved upon time . The results of this research contribute to both the methodological and the empirical perspectives : We examined the effectiveness of the dynamic time warping ( DTW ) distances in measuring the differences between long-term mass media data . We identified four types of connection strength patterns between China and its top 15 related countries . One of the advantages of the kernel methods is that they can deal with various kinds of objects , not necessarily vectorial data with a fixed number of attributes . In this paper , we develop kernels for time series data using dynamic time warping ( DTW ) distances . Since DTW distances are pseudo distances that do not satisfy the triangle inequality , a kernel matrix based on them is not positive semidefinite , in general . The main contribution of this paper is to reveal what is the best constraint for handwriting recognition by using the non-Markovian DTW . The result showed that the best constraint is not a Markovian but a totally non-Markovian constraint that regulates the matching between very distant points ; that is , it was proved that the conventional Markovian DTW has a clear limitation and the non- Markovian DTW should be more focused in future research . The theoretical analysis and experiment results prove that the im-proved method is effective . At last , a DTW based real-time recognition system under Matlab platform is developed . The BoAW model gave more than 80 % recall value on 70 % top retrieval . To re-score the detection , we further employ DTW search or modified DTW search to retrieve the spoken query from the selected utterances using BoAW model . QbE-STD experiments are conducted on different international benchmarks , namely , MediaEval spoken web search SWS 2013 and MediaEval query-by-example search on speech QUESST 2014 . The algorithm is using adaptive evaporation coefficient , a new state transition rule and so on . The simulation results show that the new algorithm has better global search ability , accurateness than the traditional ant colony algorithm and the traditional DTW ; it can provide a better performance in the speech recognition rate . By combining BDTW and Adaptive Piecewise Constant Approximation ( APCA ) method , a new DTW approximation method is proposed which works for all type of time series ( even without repetition of values ) . The effectiveness of the 3D DTW and BDTW are shown on different applications , using the case studies and 85 datasets in UCR time series classification archive . However , there is a significant number of important algorithms including clustering and classification that require the pairwise distance matrix for all time series objects . The only techniques available to deal with this issue are constraint bands and DTW approximations . In this paper , we propose the first exact approach for speeding up the all-pairwise DTW matrix calculation . Best classification results were for time delays of both 30 and 45 days in California : 79.5 % for single-band DTWs and 85.6 % for multi-band DTWs . In Texas , 45 days was best for single-band DTW ( 89.1 % ) , while 30 days yielded best results for multi-band DTW ( 87.6 % ) . Using temporal information from five vegetation indices instead of one increased the overall accuracy in California with 6.1 % . This is directly related to the number of random variables required for an adequate representation of the random fields included in the PDE . With the presented new approach , we circumvent this major complexity obstacle by combining two highly efficient model reduction strategies , namely a modern low-rank tensor representation in the tensor train format of the problem and a refinement algorithm on the basis of a posteriori error estimates to adaptively adjust the different employed discretizations . The adaptive adjustment includes the refinement of the FE mesh based on a residual estimator , the problem-adapted stochastic discretization in anisotropic Legendre Wiener chaos and the successive increase of the tensor rank . Low rank tensor representation underpins much of recent progress in tensor completion . In real applications , however , this approach is confronted with two challenging problems , namely ( 1 ) tensor rank determination ; ( 2 ) handling real tensor data which only approximately fulfils the low-rank requirement . Low-rank tensor learning has many applications in machine learning . A series of batch learning algorithms have achieved great successes . Most existing algorithms for learning appearance model are vector-based methods without maintaining the 2D spatial structure information of objects in an image . To this end , a robust tensor subspace learning algorithm is developed for background modeling which can capture the appearance changes through adaptively updating the tensor subspace . In the tensor framework , the spatial structure information is maintained and utilized for feature extraction of objects . Then by incorporating the robust scheme , we can weight individual pixel of an image to reduce the influence of outliers on background modeling . Furthermore an incremental algorithm for the robust tensor subspace learning is proposed to adapt to the variation of appearance model . The experimental results illustrate the effectiveness of the proposed robust learning algorithm for anomaly detection . Here , we introduce and study a novel tensor approach for fast and accurate assembled summation of a large number of lattice-allocated potentials represented on 3D N × N × N grid with the computational requirements only weakly dependent on the number of summed potentials . It is based on the assembled low-rank canonical tensor representations of the collected potentials using pointwise sums of shifted canonical vectors representing the single generating function , say the Newton kernel . For a sum of electrostatic potentials over L × L × L lattice embedded in a box the required storage scales linearly in the 1D grid-size , O ( N ) , while the numerical cost is estimated by O ( N L ) . However , it remains a challenging task to classify the high-dimensional HSI with limited number of training samples . In this paper , we propose a spectral-spatial classification framework based on low-rank tensor learning ( lrTL ) . Unlike the traditional vector/matrix-based methods , the proposed lrTL method aims at improving the classification performance by naturally treating the HSI as a third-order tensor under the umbrella of multilinear algebra . This paper addresses the impact of partially observable measurements on short-term solar power prediction . We present a low-rank tensor learning scheme to predict six-hour-ahead solar power generation . We use actual multivariate spatio-temporal National Renewable Energy Laboratory solar data in the state of Kansas presented in the form of tensors along with a photovoltaic power conversion model . The resulting multi-variate optimization problem can be effectively solved using the augmented Lagrange multiplier ( ALM ) algorithm . Experiments demonstrate that our method results with better accuracy compared with some other state-of-the-art low-rank tensor representation learning approaches on the MSR Hand Gesture 3D database and the MSR Action 3D database . By denoising the Multi-PIE face database , our experimental setup testifies the proposed method can also be employed to recover images . In particular , we study applicability of tensor approximations for the numerical solution of Hartree-Fock and Kohn-Sham equations on 3D Cartesian grids . We show that the orthogonal Tucker-type tensor approximation of electron density and Hartree potential of simple molecules leads to low tensor rank representations . This enables an efficient tensor-product convolution scheme for the computation of the Hartree potential using a collocation-type approximation via piecewise constant basis functions on a uniform nxnxn grid . Existing models usually assume simple inter-dependence among variables , space , and time , and are computationally expensive . We propose a unified low rank tensor learning framework for multivariate spatio-temporal analysis , which can conveniently incorporate different properties in spatio-temporal data , such as spatial clustering and shared structure among variables . We demonstrate how the general framework can be applied to cokriging and forecasting tasks , and develop an efficient greedy algorithm to solve the resulting optimization problem with convergence guarantee . Coming up with a good feature - set for a parser is a hard and time consuming task , and many researchers attempt to reduce the required manual effort . The work of lei - EtAl:2014:P14 - 1 suggests a low - rank tensor representation to automatically find good feature combinations . taubtabib2015template suggest a kernel - based approach to implicitly consider all possible feature combinations over sets of core - features . Physical expertise on the calibration parameters can also be taken into account in a Bayesian framework . Finally , the method is applied to the thermal-hydraulic code FLICA 4 , in a single-phase friction model framework . It allows significant improvement of the predictions of FLICA 4 . In this paper Hualong One nuclear power plant model is established using TRACE code in detail . First according to analysis assumptions in PSAR , research of complete loss of flow accident ( CLOFA ) for Hualong One is carried out , and then research of core DNBR through the accident is carried out using FLICA III-F code . By comparison the research results and PSAR results are very consistent , it means the research method is feasible and the results are credible .
Together with a weighting function that considers the local strength of the inspected ultrasonic signals and a modified partial array reference correction , dynamic time warping proved to be a robust estimation scheme for phase aberration . This is demonstrated for aberrated synthetic and experimental data in a comparative evaluation of dynamic time warping together with well-known aberration correction techniques like cross-correlation and speckle look-back .
This paper proposes an enhanced freeway incident detection algorithm that is transferable to different freeway segments . The algorithm is a modified form of previously proposed dynamic time warping-based algorithm that utilizes the concept of pattern similarity amongst microscopic traffic variables time series while using only minimum prior knowledge . Unlike previous approaches where empirical incident precursor patterns are directly used for calibration , the proposed method uses artificial incident precursor patterns associated with probabilistic models derived from hierarchy clustering of incident precursor patterns database .
This step gives a new template of M feature vectors , where M<<N. The second step takes into account the frequency discrimination features which discriminate each word of the vocabulary from the others or a set of them . An important characteristic of this process is that the new templates do not need time alignment with the references in the comparison step , avoiding the use of the dynamic time-warping process . The speech recognition results show a significant improvement in the recognition performance with a digit database and the confusable E-set.<<ETX > >
The proposed method is time efficient as it is based on histograms of action poses , extracted from MoCap data , that are computed according to Hausdorff distance . The histograms are then compared with the Bhattacharyya distance and warped by a dynamic time warping process to achieve their optimal alignment . This process , implemented by our dynamic programming-based solution , has the advantage of allowing some stretching flexibility to accommodate for possible action length changes .
This study presents preliminary results on the use of dynamic time warping to distinguish between the effects of vowel identity and postvocalic consonant voicing on the formant trajectories of English front vowels . Using PraatR ( Albin 2014 ) , formant trajectories are extracted from sound files in Praat and imported into R , where the dynamic time warping analysis is conducted using the dtw package ( Giorgino 2009 ) . Albin , A. L. ( 2014 ) .
Kinematic variables were computed from the position , speed and acceleration time series . Two-way ANOVA , Passing – Bablok regressions , and dynamic time warping analysis showed good to excellent agreement between Elite and DYSKIMOT , both at the qualitative level of the time series shape and at the quantitative level of peculiar kinematical events ’ measured values . In conclusion , DYSKIMOT sensor is as relevant as a gold-standard system to assess kinematical features during fast head rotations in participants and patients , demonstrating its usefulness in both clinical practice and research environments .
Histogram Equalization is used for contrast stretching , Gabor wavelets and Kernel 2D PCA is used to extract local and nonlinear features and those are invariant towards orientation & spatial locality . Those features are fused with PCA fusion and then Two Dimensional Dynamic Time Warping is used for the classification of those feature vectors . The constraints Continuity , Monotonicity and bounded properties of DTW will identify the non linear optimal path between two dimensions of feature vectors simultaneously .
Battery capacity estimation is a significant recent challenge given the complex physical and chemical processes that occur within batteries and the restrictions on the accessibility of capacity degradation data . In this study , we describe an approach called dynamic spatial time warping , which is used to determine the similarities of two arbitrary curves . Unlike classical dynamic time warping methods , this approach can maintain the invariance of curve similarity to the rotations and translations of curves , which is vital in curve similarity search .
Second , a spectral time-frequency analysis segmentation approach is employed to generalize all potential time-variant lane-change and lane-keeping candidates . The final stage compares two possible classification methods—1 ) dynamic time warping feature with k -nearest neighbor classifier and 2 ) hidden state sequence prediction with a combined hidden Markov model . The overall optimal classification accuracy can be obtained at 80.36 % for lane-change-left and 83.22 % for lane-change-right .
The authors are researching on the new cognitive capability enabling robots to judge about the correctness of the rehabilitative exercise performed by patients following the robot 's indications . The proposed method uses the Dynamic Time Warping functionality comparing the correct sequence ( encoded in the Knowledge Base ) with the human actions being observed by the robot 's eyes . The proposed approach is an enabler of better sustainable rehabilitative care services in remote residential settings because of lowering the need of human care .
In this paper a new approach based on Gaussian mixture hidden Markov models ( GHMM ) is introduced , evaluated and compared with traditional approach for isolated word recognition system . A new parameter is introduced in the HMM as defined as the prototype word represented the class word , is calculated in the training phase by the iterative algorithm based the dynamic warping time technique ( DTW ) . The full and diagonal covariance matrix is used for word recognition .
Early applications of neural nets to speech recognition used Time - Delay Neural Nets which can be seen as simple forms of CNNs without pooling or subsampling . Full - fledged CNNs with pooling and subsampling were soon applied to speech recognition and combined with dynamic time warping . While the globally - trained combination of neural nets and HMMs for speech and handwriting goes back to the 1990s , only due to recent developments HMM / DNN hybrid modeling became dominant in ASR .
Time Series Analysis is a statistical methodology for analyzing time series data , which are data points measured over time , in order to extract data characteristics and meaningful information . Time Series Analysis is an important subject because time series data are generated in many disciplines , e.g. , economics , engineering , health science , meteorology , etc .
It is important to learn the different overlapping patterns in the long term and short term time series data . Conventional methods are based on analysing time series data using only one time series analysis technique . The principal aim of this Thesis is to examine the concurrent overlapping features of time-series data using an improved version of three important time-series analysis techniques such as Locally Weighted Scatterplot Smoothing ( LOWESS and the related algorithm LOESS ) , change point , trend , and control chart patterns .
Time-series data analysis is essential in many modern applications , such as financial markets , sensor networks , and data centers , and correlation discovery is a core technique for the analysis . In this paper , we address a novel problem that computes a k-sized time-series dataset where the minimum Pearson correlation of any two time-series in the set is maximized .
An initial evaluation with SensorScope , an outdoor temperature dataset , confirms that our solution is able to detect and classify faulty readings into four fault types , namely , 1 ) random , 2 ) mal-function , 3 ) bias , and 4 ) drift with accuracy up to 95 % . The results also show that , with the experimental dataset , the time series data analysis technique performs comparable well in most of the cases , whilst in some other cases the support from neighbourhood voting technique and histogram analysis helps our hybrid solution to successfully detects the faults of all types .
We propose a distributed medium access control protocol for cognitive radio networks to opportunistically utilize multiple channels . Under the proposed protocol , cognitive radio nodes forecast and rank channel availability observing primary users ’ activities on the channels for a period of time by time series analyzing using smoothing models for seasonal data by Winters ’ method . The proposed approach protects primary users , mitigates channel access delay , and increases network performance .
However , they also pose a disadvantage as successful insertion into the stratum corneum layer of the skin relies on sufficient mechanical strength of the biodegradable material . This review looks at the various technologies developed in MN research and shows the rapidly growing numbers of research papers and patent publications since the first invention of MNs ( using time series statistical analysis ) . This provides the research and industry communities a valuable synopsis of the trends and progress being made in this field .
Hotspot can also be defined as an overloaded condition of the Physical Machine . Our paperaims at resolving the issues of the imbalance of resource utilization using “ A Time Series based Analysis Technique ” . It is used for the effective utilization of resources and to retain the downtime during the detection of Hotspots in case of occurrence of any sudden spikes .
Forecasting model of coal rock electromagnetic radiation was built with combining time series analyze and adaptive neuro-fuzzy inference system ( ANFIS ) . In the first , coal rock electromagnetic radiation phase space was reconstructed through Takens theory , and time delay and embedding dimension are determined by mutual information method and false nearest neighbor method respectively .
A short summary of the world-system perspective on international relations suggests a number of hypotheses regarding processes that operate at the level of the whole system . The feasibility of testing these propositions with the time-series method of analysis of a single case is discussed , and problems arising from the use of this research design are considered . There are eight anticipated problems regarding operationalization and measurement : ( 1 ) bounding and mapping the system and its constituent zones ; ( 2 ) validity and reliability of measures over time ; ( 3 ) limitations of aggregating data on nation-states to create contextual world-system variables ; ( 4 ) transformation of nation-state data to make it more suitable for world-system research ; ( 5 ) combining data from different sources in the same indicator ; ( 6 ) noncontinuous data series ; ( 7 ) the width of a time point and measurement error in time ; and ( 8) the small number of instances of long-run processes over the 500-year history of the world-system .
These operations are efficient to cope with broken and touching characters in words . The distortion of character shapes is handled by coupling the string matching process with local shape comparisons that are achieved by Dynamic Time Warping ( DTW ) . The costs of the SDE operations are provided by the DTW distances .
To produce reliable matching results even though there are large translation and rotation of a sensor , we proposed a method that combines the advantages of sum of squared difference ( SSD ) and dynamic time warping ( DTW ) . Dominant corresponding feature pairs are found using a proximity matrix and an SSD-based similarity matrix , and then the remaining feature matching is accomplished by DTW . Experimental results show that a zero failure rate of matching can be achieved in an indoor environment if the baseline is less than 20 cm .
To address the accuracy and real-time performance problem of human identification , a fast and robust ECG-based identification algorithm was proposed in this study , which was particularly suitable for miniaturized embedded platforms . Firstly , a dynamic-threshold method was used to extract stable ECG waveforms as template samples and test samples ; then , based on a modified Dynamic Time Warping ( DTW ) method , the degree of difference between matching samples was calculated to reach a result of recognition . Considering that ECG is a kind of timevarying and non-stationary signals , ECG template database should be dynamically updated to ensure the consistency of the template and body status and further improve recognition accuracy and robustness .
The Global Data on Events , Location and Tone ” ( GDELT ) to model the image of China in mass media , specifically , how China has related to the rest of the world and how this connection has evolved upon time . The results of this research contribute to both the methodological and the empirical perspectives : We examined the effectiveness of the dynamic time warping ( DTW ) distances in measuring the differences between long-term mass media data . We identified four types of connection strength patterns between China and its top 15 related countries .
One of the advantages of the kernel methods is that they can deal with various kinds of objects , not necessarily vectorial data with a fixed number of attributes . In this paper , we develop kernels for time series data using dynamic time warping ( DTW ) distances . Since DTW distances are pseudo distances that do not satisfy the triangle inequality , a kernel matrix based on them is not positive semidefinite , in general .
The main contribution of this paper is to reveal what is the best constraint for handwriting recognition by using the non-Markovian DTW . The result showed that the best constraint is not a Markovian but a totally non-Markovian constraint that regulates the matching between very distant points ; that is , it was proved that the conventional Markovian DTW has a clear limitation and the non- Markovian DTW should be more focused in future research .
The theoretical analysis and experiment results prove that the im-proved method is effective . At last , a DTW based real-time recognition system under Matlab platform is developed .
The BoAW model gave more than 80 % recall value on 70 % top retrieval . To re-score the detection , we further employ DTW search or modified DTW search to retrieve the spoken query from the selected utterances using BoAW model . QbE-STD experiments are conducted on different international benchmarks , namely , MediaEval spoken web search SWS 2013 and MediaEval query-by-example search on speech QUESST 2014 .
The algorithm is using adaptive evaporation coefficient , a new state transition rule and so on . The simulation results show that the new algorithm has better global search ability , accurateness than the traditional ant colony algorithm and the traditional DTW ; it can provide a better performance in the speech recognition rate .
By combining BDTW and Adaptive Piecewise Constant Approximation ( APCA ) method , a new DTW approximation method is proposed which works for all type of time series ( even without repetition of values ) . The effectiveness of the 3D DTW and BDTW are shown on different applications , using the case studies and 85 datasets in UCR time series classification archive .
However , there is a significant number of important algorithms including clustering and classification that require the pairwise distance matrix for all time series objects . The only techniques available to deal with this issue are constraint bands and DTW approximations . In this paper , we propose the first exact approach for speeding up the all-pairwise DTW matrix calculation .
Best classification results were for time delays of both 30 and 45 days in California : 79.5 % for single-band DTWs and 85.6 % for multi-band DTWs . In Texas , 45 days was best for single-band DTW ( 89.1 % ) , while 30 days yielded best results for multi-band DTW ( 87.6 % ) . Using temporal information from five vegetation indices instead of one increased the overall accuracy in California with 6.1 % .
This is directly related to the number of random variables required for an adequate representation of the random fields included in the PDE . With the presented new approach , we circumvent this major complexity obstacle by combining two highly efficient model reduction strategies , namely a modern low-rank tensor representation in the tensor train format of the problem and a refinement algorithm on the basis of a posteriori error estimates to adaptively adjust the different employed discretizations . The adaptive adjustment includes the refinement of the FE mesh based on a residual estimator , the problem-adapted stochastic discretization in anisotropic Legendre Wiener chaos and the successive increase of the tensor rank .
Low rank tensor representation underpins much of recent progress in tensor completion . In real applications , however , this approach is confronted with two challenging problems , namely ( 1 ) tensor rank determination ; ( 2 ) handling real tensor data which only approximately fulfils the low-rank requirement .
Low-rank tensor learning has many applications in machine learning . A series of batch learning algorithms have achieved great successes .
Most existing algorithms for learning appearance model are vector-based methods without maintaining the 2D spatial structure information of objects in an image . To this end , a robust tensor subspace learning algorithm is developed for background modeling which can capture the appearance changes through adaptively updating the tensor subspace . In the tensor framework , the spatial structure information is maintained and utilized for feature extraction of objects . Then by incorporating the robust scheme , we can weight individual pixel of an image to reduce the influence of outliers on background modeling . Furthermore an incremental algorithm for the robust tensor subspace learning is proposed to adapt to the variation of appearance model . The experimental results illustrate the effectiveness of the proposed robust learning algorithm for anomaly detection .
Here , we introduce and study a novel tensor approach for fast and accurate assembled summation of a large number of lattice-allocated potentials represented on 3D N × N × N grid with the computational requirements only weakly dependent on the number of summed potentials . It is based on the assembled low-rank canonical tensor representations of the collected potentials using pointwise sums of shifted canonical vectors representing the single generating function , say the Newton kernel . For a sum of electrostatic potentials over L × L × L lattice embedded in a box the required storage scales linearly in the 1D grid-size , O ( N ) , while the numerical cost is estimated by O ( N L ) .
However , it remains a challenging task to classify the high-dimensional HSI with limited number of training samples . In this paper , we propose a spectral-spatial classification framework based on low-rank tensor learning ( lrTL ) . Unlike the traditional vector/matrix-based methods , the proposed lrTL method aims at improving the classification performance by naturally treating the HSI as a third-order tensor under the umbrella of multilinear algebra .
This paper addresses the impact of partially observable measurements on short-term solar power prediction . We present a low-rank tensor learning scheme to predict six-hour-ahead solar power generation . We use actual multivariate spatio-temporal National Renewable Energy Laboratory solar data in the state of Kansas presented in the form of tensors along with a photovoltaic power conversion model .
The resulting multi-variate optimization problem can be effectively solved using the augmented Lagrange multiplier ( ALM ) algorithm . Experiments demonstrate that our method results with better accuracy compared with some other state-of-the-art low-rank tensor representation learning approaches on the MSR Hand Gesture 3D database and the MSR Action 3D database . By denoising the Multi-PIE face database , our experimental setup testifies the proposed method can also be employed to recover images .
In particular , we study applicability of tensor approximations for the numerical solution of Hartree-Fock and Kohn-Sham equations on 3D Cartesian grids . We show that the orthogonal Tucker-type tensor approximation of electron density and Hartree potential of simple molecules leads to low tensor rank representations . This enables an efficient tensor-product convolution scheme for the computation of the Hartree potential using a collocation-type approximation via piecewise constant basis functions on a uniform nxnxn grid .
Existing models usually assume simple inter-dependence among variables , space , and time , and are computationally expensive . We propose a unified low rank tensor learning framework for multivariate spatio-temporal analysis , which can conveniently incorporate different properties in spatio-temporal data , such as spatial clustering and shared structure among variables . We demonstrate how the general framework can be applied to cokriging and forecasting tasks , and develop an efficient greedy algorithm to solve the resulting optimization problem with convergence guarantee .
Coming up with a good feature - set for a parser is a hard and time consuming task , and many researchers attempt to reduce the required manual effort . The work of lei - EtAl:2014:P14 - 1 suggests a low - rank tensor representation to automatically find good feature combinations . taubtabib2015template suggest a kernel - based approach to implicitly consider all possible feature combinations over sets of core - features .
Physical expertise on the calibration parameters can also be taken into account in a Bayesian framework . Finally , the method is applied to the thermal-hydraulic code FLICA 4 , in a single-phase friction model framework . It allows significant improvement of the predictions of FLICA 4 .
In this paper Hualong One nuclear power plant model is established using TRACE code in detail . First according to analysis assumptions in PSAR , research of complete loss of flow accident ( CLOFA ) for Hualong One is carried out , and then research of core DNBR through the accident is carried out using FLICA III-F code . By comparison the research results and PSAR results are very consistent , it means the research method is feasible and the results are credible .
Phase aberration correction using dynamic time warping Generalizability and Transferability of Incident Detection Algorithm Using Dynamic Time Warping Statistical feature selection for isolated word recognition Ongoing human action recognition with motion capture Formant trajectory analysis using dynamic time warping: Preliminary results DYSKIMOT: An Ultra-Low-Cost Inertial Sensor to Assess Head’s Rotational Kinematics in Adults during the Didren-Laser Test Nonlinear Face Classification with Modified 2DDTW Similarity recognition of online data curves based on dynamic spatial time warping for the estimation of lithium-ion battery capacity Lane-Change Detection From Steering Signal Using Spectral Segmentation and Learning-Based Classification Assessment of rehabilitative exercises by humanoid robot Hybrid approach DTW/HMMC for the recognition of the isolated Arabic words Very deep multilingual convolutional neural networks for LVCSR Core Resources on Time Series Analysis for Academic Libraries: A Selected,Annotated Bibliography Identification of Overlapping Features in Time Series Data Correlation Set Discovery on Time-Series Data Applying time series analysis and neighbourhood voting in a decentralised approach for fault detection and classification in WSNs A Distributed Medium Access Control Protocol for Cognitive Radio Ad Hoc Networks Microneedles for drug delivery: trends and progress A time series based Hotspot detection in virtualized environment Forecasting Coal and Rock Dynamic Disaster Based on Adaptive Neuro-Fuzzy Inference System Comparative Research on World-System Characteristics Word spotting in historical printed documents using shape and sequence comparisons Feature matching in omnidirectional images with a large sensor motion for map generation of a mobile robot Real-time human identification algorithm based on dynamic electrocardiogram signals Exploring inter-country connection in mass media: A case study of China Learning a Kernel Matrix for Time Series Data from DTW Distances Learning non-Markovian constraints for handwriting recognition Improved DTW speech recognition method based on vector quantization and search table Partial matching and search space reduction for QbE-STD Speech dynamic time warping based on ant colony optimization algorithm Novel Approaches Towards Fast and Accurate Time series Classification Speeding Up All-Pairwise Dynamic Time Warping Matrix Calculation Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2 Adaptive stochastic Galerkin FEM with hierarchical tensor representations Beyond Low Rank: A Data-Adaptive Tensor Completion Method Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams Robust tensor subspace learning for anomaly detection Grid-based lattice summation of electrostatic potentials by assembled rank-structured tensor approximation Low-rank tensor learning for classification of hyperspectral image with limited labeled samples Multivariate Spatio-temporal Solar Generation Forecasting: A Unified Approach to Deal With Communication Failure and Invisible Sites Low-Rank Tensor Learning with Discriminant Analysis for Action Classification and Image Recovery Tensor decomposition in electronic structure calculations on 3D Cartesian grids Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations Calibration and Improved Prediction of Computer Models by Universal Kriging Research of Complete Loss of Flow Accident for Hualong One Based on Trace Code
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ICASSP PervasiveHealth 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) DEXA (2) SoICT ICCCI (2) ICONIP (2) ICDAR SDM ICML AAAI NIPS Transactions of the Association for Computational Linguistics
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