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Journal articles on the topic 'Soft-DTW'

Author: Grafiati

Published: 22 February 2025

Last updated: 31 July 2025

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1

Venkata Ramudu, Dr Balasani, Mr Chiranjeevi Kondabathini, and Mr Udaya Kiran Mandhugula. "Enhancing Handwritten Signature Identification and Palm Biometric Objectives." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–13. http://dx.doi.org/10.55041/ijsrem27802.

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Soft biometrics are already widely used as a support tool for user identification. However, it is not the only use for biometric information that is conceivable because such information can be sufficient to obtain minimal details from the user that are unrelated to his identity. Examples of what might be referred to as soft biometrics include gender, hand orientation, and emotional state. Utilizing physiologic modalities for soft-biometric work is extremely prevalent, prediction, but behavioral data is frequently disregarded. Keystroke dynamics and handwriting signature are two potential behav

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Kang, Yi, Dong Yi Chen, Michael Lawo, and Shi Ji Xia Hou. "A Wearable Swallowing Detecting Method Based on Nanometer Materials Sensor." Advances in Science and Technology 100 (October 2016): 120–29. http://dx.doi.org/10.4028/www.scientific.net/ast.100.120.

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Obesity and dysphagia are of potential and direct serious harm to the human body health. A commonly used method is controlling food intake to avoid obesity or determining if dysphagia exists by monitoring the swallow . This paper proposes a swallow detecting principle based on nanometer materials sensor, and implements a wearable detecting system with advantage of improved DTW algorithm. The system efficiently detects and faithfully identifies swallowing. In addition, it reduces the demand for hardware computing power. The system meets the features of a wearable system, such as soft and comfor

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Sun, Xiaojun, Yingbo Gao, Qiao Zhang, and Shunliang Ding. "Machine Learning-Based Extraction Method for Marine Load Cycles with Environmentally Sustainable Applications." Sustainability 16, no. 11 (2024): 4840. http://dx.doi.org/10.3390/su16114840.

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The current lack of harmonized standard test conditions for marine shipping hinders the comparison of performance and compliance assessments for different types of ships. This article puts forward a method for extracting ship loading cycles using machine learning algorithms. Time-series data are extracted from real ships in operation, and a segmented linear approximation method and a data normalization technique are adopted. A hierarchical-clustering type of soft dynamic time-warping similarity analysis method is presented to efficiently analyze the similarity of different time-series data, us

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Jiang, Yonghua, Maoli Lu, Zhilin Dong, et al. "Adaptive Deeping Siamese Residual Network: A Novel Model for Few-Shot Bearing Fault Diagnosis." Machines 13, no. 3 (2025): 193. https://doi.org/10.3390/machines13030193.

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The diagnostic performance of deep learning models is heavily reliant on large volumes of labeled training data. However, in practical applications, bearing fault samples are relatively scarce, and the availability of samples for effective model training is even more limited, leading to the suboptimal performance of traditional deep learning methods in bearing fault diagnosis. To address the issue of poor performance in few-shot bearing fault diagnosis, a novel Adaptive Deep Siamese Residual Network (ADSRN) is proposed in this study. Frequency-domain information is extracted using the Fourier

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Wang, Feng, Hongbo Lin, and Ziming Ma. "Transmission Line Icing Prediction Based on Dynamic Time Warping and Conductor Operating Parameters." Energies 17, no. 4 (2024): 945. http://dx.doi.org/10.3390/en17040945.

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Aiming to improve on the low accuracy of current transmission line icing prediction models and ignoring the objective law of icing of transmission lines, a transmission line icing prediction model considering the effect of transmission line tension on the bundle of icing thickness is proposed, based on a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU). Firstly, the finite element calculation model of the conductor and insulator system was established, and the change rule between transmission line tension and icing thickness was studied. Then, the convolutional

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Li, Qing, Xinyan Zhang, Tianjiao Ma, Dagui Liu, Heng Wang, and Wei Hu. "A Multi-step ahead photovoltaic power forecasting model based on TimeGAN, Soft DTW-based K-medoids clustering, and a CNN-GRU hybrid neural network." Energy Reports 8 (November 2022): 10346–62. http://dx.doi.org/10.1016/j.egyr.2022.08.180.

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Wu, Xuning, Qian Li, Hu Yin, et al. "Real-Time Intelligent Recognition Method for Horizontal Well Marker Bed." Mathematical Problems in Engineering 2020 (June 17, 2020): 1–8. http://dx.doi.org/10.1155/2020/8583943.

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The accurate identification of the horizontal well marker bed is to guarantee the soft landing of the well trajectory. With the intelligent development of the petroleum industry, it is feasible to apply computers to identify the marker bed automatically. In case-based reasoning technology, the data of well logging while drilling (LWD) as characteristic parameters are compared with those of adjacent well. By taking the depth sequence of LWD data as time series and using Dynamic Time Warping (DTW) similarity measure algorithm, the similarity index of each drilling depth is calculated correspondi

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Du, Yanling, Jiahao Huang, Jiasheng Chen, Ke Chen, Jian Wang, and Qi He. "Enhanced Transformer Framework for Multivariate Mesoscale Eddy Trajectory Prediction." Journal of Marine Science and Engineering 12, no. 10 (2024): 1759. http://dx.doi.org/10.3390/jmse12101759.

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Accurately predicting the trajectories of mesoscale eddies is essential for comprehending the distribution of marine resources and the multiscale energy cascade in the ocean. Nevertheless, current approaches for predicting mesoscale eddy trajectories frequently exhibit inadequate examination of the intrinsic multiscale temporal data, resulting in diminished predictive precision. To address this challenge, our research introduces an enhanced transformer-based framework for predicting mesoscale eddy trajectories. Initially, a multivariate dataset of mesoscale eddy trajectories is constructed and

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Vuckovic, C., A. Cremer, C. Minsart, et al. "P0367 A Clustering approach to discriminate slow and rapid biologics switchers in difficult-to-treat Crohn’s Disease patients." Journal of Crohn's and Colitis 19, Supplement_1 (2025): i842—i844. https://doi.org/10.1093/ecco-jcc/jjae190.0541.

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Abstract Background CD patients exhibit highly variable responses to biologics. While some patients achieve sustained remission with only one biologic over the course of their disease, other will require the sequencing of multiple biologics (“difficult-to-treat” patients) for optimal disease control. The aim of this study was to delineate the profile of treatment regimen of the biological exposed CD patients with luminal disease. Methods Among CD patients diagnosed between 1999 and 2019 with B1 phenotype at diagnosis, 203 patients who maintained this phenotype at maximum follow-up (FU, median

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Chen, Yuyao, Christian Obrecht, and Frédéric Kuznik. "Enhancing peak prediction in residential load forecasting with soft dynamic time wrapping loss functions." Integrated Computer-Aided Engineering, January 25, 2024, 1–14. http://dx.doi.org/10.3233/ica-230731.

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Short-term residential load forecasting plays a crucial role in smart grids, ensuring an optimal match between energy demands and generation. With the inherent volatility of residential load patterns, deep learning has gained attention due to its ability to capture complex nonlinear relationships within hidden layers. However, most existing studies have relied on default loss functions such as mean squared error (MSE) or mean absolute error (MAE) for neural networks. These loss functions, while effective in overall prediction accuracy, lack specialized focus on accurately predicting load peaks

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Ma, Yan, Yiou Tang, Yang Zeng, Tao Ding, and Yifu Liu. "An N400 identification method based on the combination of Soft-DTW and transformer." Frontiers in Computational Neuroscience 17 (February 16, 2023). http://dx.doi.org/10.3389/fncom.2023.1120566.

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As a time-domain EEG feature reflecting the semantic processing of the human brain, the N400 event-related potentials still lack a mature classification and recognition scheme. To address the problems of low signal-to-noise ratio and difficult feature extraction of N400 data, we propose a Soft-DTW-based single-subject short-distance event-related potential averaging method by using the advantages of differentiable and efficient Soft-DTW loss function, and perform partial Soft-DTW averaging based on DTW distance within a single-subject range, and propose a Transformer-based ERP recognition clas

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Liu, Yingchang, Jie Tang, Zhengwei Tang, and Chengyu Sun. "Robust full-waveform inversion based on automatic differentiation and differentiable dynamic time warping." Journal of Geophysics and Engineering, April 18, 2023. http://dx.doi.org/10.1093/jge/gxad029.

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Abstract Full waveform inversion is a methodology that determines high-resolution parameters. The widely used L2-norm misfit function has local minima if the low wavenumber components are not accurate. Suffering from cycle skipping problem, the solution of waveform inversion will be trapped in the local minima. Dynamic time warping aims to find an optimal alignment between two signals, which is a more robust measure to avoid cycle-skipping challenges. However, the discontinuity makes the conventional dynamic time warping distance unsuitable for waveform inversion. We introduce a soft dynamic t

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13

Tan, Jiayan, Weitao Wang, and Charles Langston. "Full Waveform Inversion Based on Dynamic Time Warping and Application to Reveal the Crustal Structure of Western Yunnan, Southwest China." Journal of Geophysical Research: Solid Earth 129, no. 9 (2024). http://dx.doi.org/10.1029/2024jb029303.

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AbstractWe develop a 3D full waveform inversion (FWI) method based on dynamic time warping (DTW) to address the issue of cycle‐skipping, which can prohibit the convergence of conventional FWI methods. DTW globally compares data samples at different time steps in 2D matrices against the time shifts of waveforms. We introduce the concept of shape descriptors into softDTW, creating a soft‐shapeDTW objective function within our waveform inversion process to improve alignment accuracy. Additionally, including constraints from Sakoe‐Chiba bands in the inversion further enhances efficiency and overal

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14

Jiang, Jiajia, Songxuan Lai, Lianwen Jin, and Yecheng Zhu. "DsDTW: Local Representation Learning with Deep soft-DTW for Dynamic Signature Verification." IEEE Transactions on Information Forensics and Security, 2022, 1. http://dx.doi.org/10.1109/tifs.2022.3180219.

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15

Trelinski, Jacek, and Bogdan Kwolek. "CNN-based and DTW features for human activity recognition on depth maps." Neural Computing and Applications, May 12, 2021. http://dx.doi.org/10.1007/s00521-021-06097-1.

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AbstractIn this work, we present a new algorithm for human action recognition on raw depth maps. At the beginning, for each class we train a separate one-against-all convolutional neural network (CNN) to extract class-specific features representing person shape. Each class-specific, multivariate time-series is processed by a Siamese multichannel 1D CNN or a multichannel 1D CNN to determine features representing actions. Afterwards, for the nonzero pixels representing the person shape in each depth map we calculate statistical features. On multivariate time-series of such features we determine

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16

Meattini, Roberto, Alessandra Bernardini, Gianluca Palli, and Claudio Melchiorri. "sEMG-Based Minimally Supervised Regression Using Soft-DTW Neural Networks for Robot Hand Grasping Control." IEEE Robotics and Automation Letters, 2022, 1–8. http://dx.doi.org/10.1109/lra.2022.3193247.

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17

Munoz-Montoro, Antonio Jesús, Julio José Carabias-Orti, Pedro Vera-Candeas, Francisco Jesús Canadas-Quesada, and Nicolás Ruiz-Reyes. "Online/offline score informed music signal decomposition: application to minus one." EURASIP Journal on Audio, Speech, and Music Processing 2019, no. 1 (2019). http://dx.doi.org/10.1186/s13636-019-0168-6.

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AbstractIn this paper, we propose a score-informed source separation framework based on non-negative matrix factorization (NMF) and dynamic time warping (DTW) that suits for both offline and online systems. The proposed framework is composed of three stages: training, alignment, and separation. In the training stage, the score is encoded as a sequence of individual occurrences and unique combinations of notes denoted as score units. Then, we proposed a NMF-based signal model where the basis functions for each score unit are represented as a weighted combination of spectral patterns for each no

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18

Zhao, Mengyi, Hao Tang, Pan Xie, Shuling Dai, Nicu Sebe, and Wei Wang. "Bidirectional Transformer GAN for Long-Term Human Motion Prediction." ACM Transactions on Multimedia Computing, Communications, and Applications, January 10, 2023. http://dx.doi.org/10.1145/3579359.

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The mainstream motion prediction methods usually focus on short-term prediction, and their predicted long-term motions often fall into an average pose, i.e. the freezing forecasting problem [27]. To mitigate this problem, we propose a novel Bidirectional Transformer-based Generative Adversarial Network (BiTGAN) for long-term human motion prediction. The bidirectional setup leads to consistent and smooth generation in both forward and backward directions. Besides, to make full use of the history motions, we split them into two parts. The first part is fed to the Transformer encoder in our BiTGA

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19

Li, Weide, Zihan Hao, and Zhihe Zhang. "An Interpretable Time Series Clustering Neural Network Based on Shape Feature Extraction." International Journal of Pattern Recognition and Artificial Intelligence, October 27, 2022. http://dx.doi.org/10.1142/s0218001422540222.

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Time series is a very common but important data type. A large number of time series data are generated in various professional research fields and daily life. Although there are many models being developed to deal with time series, the cluster methods for time series are insufficient and need to improve. This paper is focused on time series clustering, which uses deep learning approach to discover the shape characteristics of time series. We establish a new neural network model of time series clustering to jointly optimize the representation learning and clustering tasks of time series. Focusi

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20

li, qing, and zhang xinyan. "Multi-Step Ahead Photovoltaic Power Forecasting Model Based on Timegan, Soft Dtw-Based K-Medoids Clustering, And a Cnn-Gru Hybrid Neural Network." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4017353.

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21

Zhao, Yu, and Hanshan Li. "A Denoising and Recognition Matching Algorithm of Projectile Signal in Infrared Light Screens Based on HOA‐VMD." Microwave and Optical Technology Letters 67, no. 3 (2025). https://doi.org/10.1002/mop.70166.

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ABSTRACTAiming at the problem of low detection ability and low signal‐to‐noise ratio of infrared light screens under strong background light conditions, this paper proposes a denoising and recognition matching method of projectile signal in infrared light screens based on HOA‐VMD under strong background light conditions. It combines the hiking optimization algorithm (HOA) and the variational mode decomposition (VMD) to decompose the signal and extract the effective components, then the wavelet layered threshold function is used for multi‐scale denoising to effectively filter out the noise inte

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22

Bünger, Dominik, Miriam Gondos, Lucile Peroche, and Martin Stoll. "An Empirical Study of Graph-Based Approaches for Semi-supervised Time Series Classification." Frontiers in Applied Mathematics and Statistics 7 (January 20, 2022). http://dx.doi.org/10.3389/fams.2021.784855.

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Time series data play an important role in many applications and their analysis reveals crucial information for understanding the underlying processes. Among the many time series learning tasks of great importance, we here focus on semi-supervised learning based on a graph representation of the data. Two main aspects are studied in this paper. Namely, suitable distance measures to evaluate the similarities between different time series, and the choice of learning method to make predictions based on a given number of pre-labeled data points. However, the relationship between the two aspects has

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23

Arfanuzzaman, Md, S. M. Tanvir Hassan, and Md Abu Syed. "Cost-benefit of promising adaptations for resilient development in climate hotspots: evidence from lower Teesta basin in Bangladesh." Journal of Water and Climate Change, January 27, 2020. http://dx.doi.org/10.2166/wcc.2020.130.

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Abstract It is very likely that climate change will increase the frequency and intensity of extreme events such as floods, flash floods, storms, heat and cold waves, riverbank erosion, and drought in the river basin of Hindu Kush Himalayan (HKH) region. This could mean detrimental impacts to the poor and marginal people in the lower Teesta basin (LTB) in Bangladesh. Though adaptation involves financial costs, the farmers' practicing adaptation in LTB experience diminished crop loss and damage. This study was aimed at assessing the promising adaptation practices, their economic return, and soci

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Kaufmann, Dan, Ariel Tikotsky, Tanhum Yoreh, and Anat Tchetchik. "Engaging faith-based communities in pro-environmental behavior using soft regulations: The case of single-use plastics." Frontiers in Environmental Science 10 (January 4, 2023). http://dx.doi.org/10.3389/fenvs.2022.1019904.

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The consumption of single-use plastics, such as disposable tableware (DTW), conveys a high benefit-cost ratio for consumers while having large environmental externalities. To encourage consumers to reduce their use of DTWs, governments could use small and non-coercive changes in people’s decision-making environments (nudges). This study focuses on the Israeli ultra-Orthodox communities a secluded population group that grows much faster- and consumes much more DTW than the rest of the Israeli population. Employing a quasi-representative sample (N = 450) of this population, this study conducted

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25

Tomyslav, Sledevič, Serackis Artūras, Tamulevičius Gintautas, and Navakauskas Dalius. "Evaluation of Features Extraction Algorithms for a Real-Time Isolated Word Recognition System." November 5, 2013. https://doi.org/10.5281/zenodo.1089188.

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Paper presents an comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in speaker dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signal to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and

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