Relevant bibliographies by topics / Kernel warping

Academic literature on the topic 'Kernel warping'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Kernel warping.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Kernel warping"

1

Zhou, Zhengyi, and David S. Matteson. "Predicting Melbourne ambulance demand using kernel warping." Annals of Applied Statistics 10, no. 4 (December 2016): 1977–96. http://dx.doi.org/10.1214/16-aoas961.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Pilario, Karl Ezra, Alexander Tielemans, and Elmer-Rico E. Mojica. "Geographical discrimination of propolis using dynamic time warping kernel principal components analysis." Expert Systems with Applications 187 (January 2022): 115938. http://dx.doi.org/10.1016/j.eswa.2021.115938.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Mishra, Piyush, and Piyush Lotia. "Speaker Recognition Using Dynamic Time Warping Polynomial Kernel SVM with Confusion Matrix." i-manager's Journal on Computer Science 3, no. 3 (November 15, 2015): 23–27. http://dx.doi.org/10.26634/jcom.3.3.3662.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Chen, Zhicheng, Yuequan Bao, Hui Li, and Billie F. Spencer. "A novel distribution regression approach for data loss compensation in structural health monitoring." Structural Health Monitoring 17, no. 6 (December 8, 2017): 1473–90. http://dx.doi.org/10.1177/1475921717745719.

Full text
Abstract:
Structural health monitoring has arisen as an important tool for managing and maintaining civil infrastructure. A critical problem for all structural health monitoring systems is data loss or data corruption due to sensor failure or other malfunctions, which bring into question in subsequent structural health monitoring data analysis and decision-making. Probability density functions play a very important role in many applications for structural health monitoring. This article focuses on data loss compensation for probability density function estimation in structural health monitoring using imputation methods. Different from common data, continuous probability density functions belong to functional data; the conventional distribution-to-distribution regression technique has significant potential in missing probability density function imputation; however, extrapolation and directly borrowing shape information from the covariate probability density function are the main challenges. Inspired by the warping transformation of distributions in the field of functional data analysis, a new distribution regression approach for imputing missing correlated probability density functions is proposed in this article. The warping transformation for distributions is a mapping operation used to transform one probability density function to another by deforming the original probability density function with a warping function. The shape mapping between probability density functions can be characterized well by warping functions. Given a covariate probability density function, the warping function is first estimated by a kernel regression model; then, the estimated warping function is used to transform the covariate probability density function and obtain an imputation for the missing probability density function. To address issues with poor performance when the covariate probability density function is contaminated, a hybrid approach is proposed that fuses the imputations obtained by the warping transformation approach with the conventional distribution-to-distribution regression approach. Experiments based on field monitoring data are conducted to evaluate the performance of the proposed approach. The corresponding results indicate that the proposed approach can outperform the conventional method, especially in extrapolation. The proposed approach shows good potential to provide more reliable estimation of distributions of missing structural health monitoring data.
APA, Harvard, Vancouver, ISO, and other styles
5

Kamycki, Krzysztof, Tomasz Kapuscinski, and Mariusz Oszust. "Data Augmentation with Suboptimal Warping for Time-Series Classification." Sensors 20, no. 1 (December 23, 2019): 98. http://dx.doi.org/10.3390/s20010098.

Full text
Abstract:
In this paper, a novel data augmentation method for time-series classification is proposed. In the introduced method, a new time-series is obtained in warped space between suboptimally aligned input examples of different lengths. Specifically, the alignment is carried out constraining the warping path and reducing its flexibility. It is shown that the resultant synthetic time-series can form new class boundaries and enrich the training dataset. In this work, the comparative evaluation of the proposed augmentation method against related techniques on representative multivariate time-series datasets is presented. The performance of methods is examined using the nearest neighbor classifier with the dynamic time warping (NN-DTW), LogDet divergence-based metric learning with triplet constraints (LDMLT), and the recently introduced time-series cluster kernel (NN-TCK). The impact of the augmentation on the classification performance is investigated, taking into account entire datasets and cases with a small number of training examples. The extensive evaluation reveals that the introduced method outperforms related augmentation algorithms in terms of the obtained classification accuracy.
APA, Harvard, Vancouver, ISO, and other styles
6

Ahmed, Rehan, Andriy Temko, William P. Marnane, Geraldine Boylan, and Gordon Lightbody. "Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel." Computers in Biology and Medicine 82 (March 2017): 100–110. http://dx.doi.org/10.1016/j.compbiomed.2017.01.017.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Nasonov, A., A. Krylov, and D. Lyukov. "IMAGE SHARPENING WITH BLUR MAP ESTIMATION USING CONVOLUTIONAL NEURAL NETWORK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (May 9, 2019): 161–66. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-161-2019.

Full text
Abstract:
<p><strong>Abstract.</strong> We propose a method for choosing optimal values of the parameters of image sharpening algorithm for out-of-focus blur based on grid warping approach. The idea of the considered sharpening algorithm is to move pixels from the edge neighborhood towards the edge centerlines. Compared to traditional deblurring algorithms, this approach requires only scalar blur level value rather than a blur kernel. We propose a convolutional neural network based algorithm for estimating the blur level value.</p>
APA, Harvard, Vancouver, ISO, and other styles
8

Ren, Zhiming, Qianzong Bao, and Bingluo Gu. "Joint wave-equation traveltime inversion of diving/direct and reflected waves for P- and S-wave velocity macromodel building." GEOPHYSICS 86, no. 4 (July 1, 2021): R603—R621. http://dx.doi.org/10.1190/geo2020-0762.1.

Full text
Abstract:
Full-waveform inversion (FWI) suffers from the local minima problem and requires a sufficiently accurate starting model to converge to the correct solution. Wave-equation traveltime inversion (WETI) is an effective tool to retrieve the long-wavelength components of the velocity model. We have developed a joint diving/direct and reflected wave WETI (JDRWETI) method to build P- and S-wave velocity macromodels. We estimate the traveltime shifts of seismic events (diving/direct waves and PP- and PS-reflections) through the dynamic warping scheme and construct a misfit function using the time shifts of diving/direct and reflected waves. We derive the adjoint wave equations and the gradients with respect to the background models based on the joint misfit function. We apply the kernel decomposition scheme to extract the kernel of the diving/direct wave and the tomography kernels of PP- and PS-reflections. For an explosive source, the kernels of the diving/direct wave and PP-reflections and the kernel of the PS-reflections are used to compute the P- and S-wave gradients of the background models, respectively. We implement JDRWETI by a two-stage inversion workflow: First, we invert the P- and S-wave velocity models using the P-wave gradients, and then we improve the S-wave velocity model using the S-wave gradients. Numerical tests on synthetic and field data sets reveal that the JDRWETI method successfully recovers the long-wavelength components of P- and S-wave velocity models, which can be used for an initial model for the subsequent elastic FWI. Moreover, the JDRWETI method prevails over the existing reflection WETI method and the cascaded diving/direct and reflected wave WETI method, especially when large velocity errors are present in the shallow part of the starting models. The JDRWETI method with the two-stage inversion workflow can give rise to reasonable inversion results even for the model with different P- and S-wave velocity structures.
APA, Harvard, Vancouver, ISO, and other styles
9

Jeong, Young-Seon. "Semiconductor Wafer Defect Classification Using Support Vector Machine with Weighted Dynamic Time Warping Kernel Function." Industrial Engineering & Management Systems 16, no. 3 (September 30, 2017): 420–26. http://dx.doi.org/10.7232/iems.2017.16.3.420.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Jeong, Young-Seon, and Raja Jayaraman. "Support vector-based algorithms with weighted dynamic time warping kernel function for time series classification." Knowledge-Based Systems 75 (February 2015): 184–91. http://dx.doi.org/10.1016/j.knosys.2014.12.003.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Kernel warping"

1

Bai, Lu, Luca Rossi, Lixin Cui, and Edwin R. Hancock. "A Nested Alignment Graph Kernel Through the Dynamic Time Warping Framework." In Graph-Based Representations in Pattern Recognition, 59–69. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58961-9_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Bagheri, Mohammad Ali, Qigang Gao, and Sergio Escalera. "Action Recognition by Pairwise Proximity Function Support Vector Machines with Dynamic Time Warping Kernels." In Advances in Artificial Intelligence, 3–14. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34111-8_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ahmed, Ibrahim, Enrico Zio, and Gyunyoung Heo. "Fault Detection by Signal Reconstruction in Nuclear Power Plants." In Nuclear Reactors [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.101276.

Full text
Abstract:
In this work, the recently developed auto associative bilateral kernel regression (AABKR) method for on-line condition monitoring of systems, structures, and components (SSCs) during transient process operation of a nuclear power plant (NPP) is improved. The advancement enhances the capability of reconstructing abnormal signals to the values expected in normal conditions during both transient and steady-state process operations. The modification introduced to the method is based on the adoption of two new approaches using dynamic time warping (DTW) for the identification of the time position index (the position of the nearest vector within the historical data vectors to the current on-line query measurement) used by the weighted-distance algorithm that captures temporal dependences in the data. Applications are provided to a steady-state numerical process and a case study concerning sensor signals collected from a reactor coolant system (RCS) during start-up operation of a NPP. The results demonstrate the effectiveness of the proposed method for fault detection during steady-state and transient operations.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Kernel warping"

1

Bai, Lu, Lixin Cui, Yue Wang, Yuhang Jiao, and Edwin R. Hancock. "A Quantum-inspired Entropic Kernel for Multiple Financial Time Series Analysis." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/614.

Full text
Abstract:
Network representations are powerful tools for the analysis of time-varying financial complex systems consisting of multiple co-evolving financial time series, e.g., stock prices, etc. In this work, we develop a new kernel-based similarity measure between dynamic time-varying financial networks. Our ideas is to transform each original financial network into quantum-based entropy time series and compute the similarity measure based on the classical dynamic time warping framework associated with the entropy time series. The proposed method bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks abstracted from financial time series of New York Stock Exchange (NYSE) database demonstrate that our approach can effectively discriminate the abrupt structural changes in terms of the extreme financial events.
APA, Harvard, Vancouver, ISO, and other styles
2

Lei, Hansheng, and Bingyu Sun. "A Study on the Dynamic Time Warping in Kernel Machines." In 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System SITIS. IEEE, 2007. http://dx.doi.org/10.1109/sitis.2007.112.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Hamilton-Wright, Andrew, and Daniel W. Stashuk. "Improved MUP Template Estimation Using Local Time Warping and Kernel Weighted Averaging." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8512886.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Damoulas, Theodoros, Samuel Henry, Andrew Farnsworth, Michael Lanzone, and Carla Gomes. "Bayesian Classification of Flight Calls with a Novel Dynamic Time Warping Kernel." In 2010 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2010. http://dx.doi.org/10.1109/icmla.2010.69.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Akyash, Mohammad, Hoda Mohammadzade, and Hamid Behroozi. "A Dynamic Time Warping Based Kernel for 3D Action Recognition Using Kinect Depth Sensor." In 2020 28th Iranian Conference on Electrical Engineering (ICEE). IEEE, 2020. http://dx.doi.org/10.1109/icee50131.2020.9260988.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Wan, Vincent, and James Carmichael. "Polynomial dynamic time warping kernel support vector machines for dysarthric speech recognition with sparse training data." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-853.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Hu, Pengchao, Guijun Ma, Yong Zhang, Cheng Cheng, Beitong Zhou, and Ye Yuan. "State of health estimation for lithium-ion batteries with dynamic time warping and deep kernel learning model." In 2020 European Control Conference (ECC). IEEE, 2020. http://dx.doi.org/10.23919/ecc51009.2020.9143757.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Sambasivan, Lokesh Kumar, Venkataramana Bantwal Kini, Srikanth Ryali, Joydeb Mukherjee, and Dinkar Mylaraswamy. "Comparison of a Few Fault Diagnosis Methods on Sparse Variable Length Time Series Sequences." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27843.

Full text
Abstract:
Accurate gas turbine engine Fault Detection and Diagnosis (FDD) is essential to improving aircraft safety as well as in reducing airline costs associated with delays and cancellations. This paper compares broadly three methods of fault detection and diagnosis (FDD) dealing with variable length time sequences. Chosen methods are based on Dynamic Time Warping (DTW), k-Nearest Neighbor method, Hidden Markov Model (HMM) and a Support Vector Machine (SVM) which makes use of DTW ingeniously as its kernel. The time sequences are obtained from Turbo Propulsion Engines in their nominal conditions and two faulty conditions. Typically there is paucity of faulty exemplars and the challenge is to come up with algorithms which work reasonably well under such circumstances. Also, normalization of data plays a significant role in determining the performance of the classifiers used for FDD in terms of their detection rate and false positives. In particular spherical normalization has been explored considering the advantage of its superior normalization properties. Given sparse training data how well each of these algorithms performs is shown by means of tests performed on time series data collected at normal and faulty modes from a turbofan gas turbine propulsion engine and the results are presented.
APA, Harvard, Vancouver, ISO, and other styles
9

Nagendar, G., and C. V. Jawahar. "Fast approximate dynamic warping kernels." In the Second ACM IKDD Conference. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2732587.2732592.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Kernel warping' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography