Relevant bibliographies by topics / Empirical mode decomposition (EMD)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Empirical mode decomposition (EMD)'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Empirical mode decomposition (EMD)"

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TSUI, PO-HSIANG, CHIEN-CHENG CHANG, and NORDEN E. HUANG. "NOISE-MODULATED EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 02, no. 01 (2010): 25–37. http://dx.doi.org/10.1142/s1793536910000410.

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The empirical mode decomposition (EMD) is the core of the Hilbert–Huang transform (HHT). In HHT, the EMD is responsible for decomposing a signal into intrinsic mode functions (IMFs) for calculating the instantaneous frequency and eventually the Hilbert spectrum. The EMD method as originally proposed, however, has an annoying mode mixing problem caused by the signal intermittency, making the physical interpretation of each IMF component unclear. To resolve this problem, the ensemble EMD (EEMD) was subsequently developed. Unlike the conventional EMD, the EEMD defines the true IMF components as t
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NIAZY, R. K., C. F. BECKMANN, J. M. BRADY, and S. M. SMITH. "PERFORMANCE EVALUATION OF ENSEMBLE EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 01, no. 02 (2009): 231–42. http://dx.doi.org/10.1142/s1793536909000102.

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Empirical mode decomposition (EMD) is an adaptive, data-driven algorithm that decomposes any time series into its intrinsic modes of oscillation, which can then be used in the calculation of the instantaneous phase and frequency. Ensemble EMD (EEMD), where the final EMD is estimated by averaging numerous EMD runs with the addition of noise, was an advancement introduced by Wu and Huang (2008) to try increasing the robustness of EMD and alleviate some of the common problems of EMD such as mode mixing. In this work, we test the performance of EEMD as opposed to normal EMD, with emphasis on the e
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Chen, Zhongzhe, Baqiao Liu, Xiaogang Yan, and Hongquan Yang. "An Improved Signal Processing Approach Based on Analysis Mode Decomposition and Empirical Mode Decomposition." Energies 12, no. 16 (2019): 3077. http://dx.doi.org/10.3390/en12163077.

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Empirical mode decomposition (EMD) is a widely used adaptive signal processing method, which has shown some shortcomings in engineering practice, such as sifting stop criteria of intrinsic mode function (IMF), mode mixing and end effect. In this paper, an improved sifting stop criterion based on the valid data segment is proposed, and is compared with the traditional one. Results show that the new sifting stop criterion avoids the influence of end effects and improves the correctness of the EMD. In addition, a novel AEMD method combining the analysis mode decomposition (AMD) and EMD is develop
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Zhou, Xiaohang, Deshan Shan, and Qiao Li. "Morphological Filter-Assisted Ensemble Empirical Mode Decomposition." Mathematical Problems in Engineering 2018 (September 17, 2018): 1–12. http://dx.doi.org/10.1155/2018/5976589.

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In the ensemble empirical mode decomposition (EEMD) algorithm, different realizations of white noise are added to the original signal as dyadic filter banks to overcome the mode mixing problems of empirical mode decomposition (EMD). However, not all the components in white noise are necessary, and the superfluous components will introduce additional mode mixing problems. To address this problem, morphological filter-assisted ensemble empirical mode decomposition (MF-EEMD) was proposed in this paper. First, a new method for determining the structuring element shape and size was proposed to impr
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Zhu, Licheng, and Abdollah Malekjafarian. "On the Use of Ensemble Empirical Mode Decomposition for the Identification of Bridge Frequency from the Responses Measured in a Passing Vehicle." Infrastructures 4, no. 2 (2019): 32. http://dx.doi.org/10.3390/infrastructures4020032.

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In this paper, ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) methods are used for the effective identification of bridge natural frequencies from drive-by measurements. A vehicle bridge interaction (VBI) model is created using the finite element (FE) method in Matlab. The EMD is employed to decompose the signals measured on the vehicle to their main components. It is shown that the bridge component of the response measured on the vehicle can be extracted using the EMD method. The influence of some factors, such as the road roughness profile and measurement
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MHAMDI, FAROUK, JEAN-MICHEL POGGI, and MÉRIEM JAÏDANE. "TREND EXTRACTION FOR SEASONAL TIME SERIES USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 03, no. 03 (2011): 363–83. http://dx.doi.org/10.1142/s1793536911000696.

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In this paper, we investigate eligibility of trend extraction through the empirical mode decomposition (EMD) and performance improvement of applying the ensemble EMD (EEMD) instead of the EMD for trend extraction from seasonal time series. The proposed method is an approach that can be applied on any time series with any time scales fluctuations. In order to evaluate our algorithm, experimental comparisons with three other trend extraction methods: EMD-energy-ratio approach, EEMD-energy-ratio approach, and the Hodrick–Prescott filter are conducted.
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BEKKA, RAÏS EL'HADI, and YAAKOUB BERROUCHE. "IMPROVEMENT OF ENSEMBLE EMPIRICAL MODE DECOMPOSITION BY OVER-SAMPLING." Advances in Adaptive Data Analysis 05, no. 03 (2013): 1350012. http://dx.doi.org/10.1142/s179353691350012x.

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The empirical mode decomposition (EMD) is a useful method for the analysis of nonlinear and nonstationary signals and found immediate applications in diverse areas of signal processing. However, the major inconvenience of EMD is the mode mixing. The ensemble EMD (EEMD) was proposed to solve the problem of mode-mixing with the assistance of added noises producing the residue noise in the signal reconstructed. The residue noise in the IMFs can be reduced with a large number of ensemble trials at the expense of the increase of computational time. Improving the computing time of the EEMD by reduci
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HUANG, JIANFENG, and LIHUA YANG. "A PIECEWISE MONOTONOUS MODEL FOR EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 05, no. 04 (2013): 1350019. http://dx.doi.org/10.1142/s1793536913500192.

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Empirical mode decomposition (EMD) lacks theoretical support. We propose a piecewise monotonous model for EMD, and prove that the trend-subtracting iteration converges and IMF-separating procedure ends up in finite steps under mild conditions. Experiments are implemented and compared with the classical EMD.
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Jiang, Xiu Shan, Rui Feng Zhang, and Liang Pan. "Short-Time Fluctuation Characteristic and Combined Forecasting of High-Speed Railway Passenger Flow Based on EEMD." Applied Mechanics and Materials 409-410 (September 2013): 1071–74. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1071.

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Take Wuhan-Guangzhou high-speed railway for example. By adopting the empirical mode decomposition (EMD) attempt to analyze mode from the perspective of volatility of high speed railway passenger flow fluctuation signal. Constructed the ensemble empirical mode decomposition-gray support vector machine (EEMD-GSVM) short-term forecasting model which fuse the gray generation and support vector machine with the ensemble empirical mode decomposition (EEMD). Finally, by the accuracy of predicted results, explains the EEMD-GSVM model has the better adaptability.
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Divya, Sathya Sree.I, and Pangedaiah.B. "A New Islanding Detection Technique using Ensemble Empirical Mode Decomposition." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 3 (2020): 461–66. https://doi.org/10.35940/ijrte.C4556.099320.

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Penetration of distributed generation (DG) is rapidly increasing but their main issue is islanding. Advanced signal processing methods needs a renewed focus in detecting islanding. The proposed scheme is based on Ensemble Empirical Mode Decomposition (EEMD) in which Gaussian white noise is added to original signal which solves the mode mixing problem of Empirical mode decomposition (EMD) and Hilbert transform is applied to obtained Intrinsic mode functions(IMF). The proposed method reliably and accurately detects disturbances at different events.
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Dissertations / Theses on the topic "Empirical mode decomposition (EMD)"

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Tomás, Ana Raquel Dias. "Application of empirical mode decomposition (EMD) to chronological series of active fires from MODIS satellite." Master's thesis, ISA/UTL, 2011. http://hdl.handle.net/10400.5/4481.

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Mestrado em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia<br>Fire is a global phenomenon, acting as an important disturbance process. Africa is one of the continents that has higher fire density, particularly in savanna regions, making it the subject of innumerous studies about fire regime and behavior. Here, a new method of time series analysis called Empirical Mode Decomposition (EMD) was applied to monthly fire counts time series from MODIS Terra/Aqua sensors. The goals were to analyze the differences between the time series from the two instruments (MODIS T
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Li, Zhendan. "An Ensemble Empirical Mode Decomposition Approach to Wear Particle Detection in Lubricating Oil Subject to Particle Overlap." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20313.

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With the development of mechanical fault diagnosis technology, complex mechanical systems do not need to be shut down periodically for the maintenance. The working condition of the mechanical systems can be monitored by analyzing the wear metal particles in the systems' lubricating oil. However, the output signals of the monitoring sensor are non-stationary. In some case the particle signals are overlapped with each other. The goal of this thesis is to find a method to decompose those overlapped particle signals, and then count the particle number in the lubricating oil. At the beginning EMD
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Abderahman, Huthaifa. "An Integrated Compensation System Based on Empirical Mode Decomposition for Robust Noninvasive Blood Pressure Estimation." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35314.

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When it comes to monitoring human health, accuracy is not a choice. Accuracy in blood pressure (BP) estimation is essential for proper diagnosis and management of hypertension. An error of 5 mmHg is so serious, it can be responsible for doubling or halving number of patients diagnosed with hypertension. Motion artifacts are external sources of inaccuracy and can be due to sudden arm motion, muscle tremor, shivering, and transport vehicle vibration. Medium term drift, due to changing environmental factors, such as ambient temperature, can also contribute to the inaccuracy. Long term drift (agei
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Šlancar, Matěj. "Potlačení driftu signálu EKG s využitím empirického rozkladu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316450.

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The aim of this thesis is to introduce with principle of Empirical Mode Decomposition method and possibility use for correction of baseline wander in ECG signals. The thesis describes the main components of the ECG signal, a selection of possible types of signal noise, its property and principles of chosen methods for filtration of ECG signals. In conclusion the evaluation of the effectiveness of the EMD method for filtering a baseline wander and it comparing with effectiveness of the linear filtration. Functionality of used algorithms has been tested on signals of CSE standard library.
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Sadeghi, Mehdi. "Potential of the Empirical Mode Decomposition to analyze instantaneous flow fields in Direct Injection Spark Ignition engine : Effect of transient regimes." Thesis, Orléans, 2017. http://www.theses.fr/2017ORLE2069/document.

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Cette étude introduit une nouvelle approche appelée Bivariate 2D-EMD pour séparer le mouvement organisé à grande échelle, soit la composante basse fréquence de l’écoulement des fluctuations turbulentes, soit la composante haute fréquence dans un champ de vitesse instantané bidimensionnel.Cette séparation nécessite un seul champ de vitesse instantané contrairement aux autres méthodes plus couramment utilisées en mécanique des fluides, comme le POD. La méthode proposée durant cette thèse est tout à fait appropriée à l’analyse des écoulements qui sont intrinsèquement instationnaires et non linéai
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Barnhart, Bradley Lee. "The Hilbert-Huang Transform: theory, applications, development." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/2670.

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Hilbert-Huang Transform (HHT) is a data analysis tool, first developed in 1998, which can be used to extract the periodic components embedded within oscillatory data. This thesis is dedicated to the understanding, application, and development of this tool. First, the background theory of HHT will be described and compared with other spectral analysis tools. Then, a number of applications will be presented, which demonstrate the capability for HHT to dissect and analyze the periodic components of different oscillatory data. Finally, a new algorithm is presented which expands HHT ability to anal
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Waindim, Mbu. "On Unsteadiness in 2-D and 3-D Shock Wave/Turbulent Boundary Layer Interactions." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511734224701396.

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Procházka, Petr. "Odstraňovaní kolísání izolinie v EKG pomocí empirické modální dekompozice." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221366.

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In this semestral thesis, realizations of chosen linear filters for baseline wander are described. These filters are then used on artificial ECG signals from CSE database with added baseline wander. These methods are compared and results are evaluated. After that, literature search of Empirical mode decomposition method is utilized. Realization of designed filters in MATLAB programming language are described, then results are evaluated with respect to filtration success.
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Ramirez, Saul Gallegos. "Toward Using Empirical Mode Decomposition to Identify Anomalies in Stream FlowData and Correlations with other Environmental Data." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7574.

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I applied empirical mode decomposition (EMD) and the Hilbert-Herbert transforms, as tools to analyze streamflow data. I used the EMD method to extract and analyze periodic processes and trends in several environmental datasets including daily stream flow, daily precipitation, and daily temperature on data from the watersheds of two rivers in the Upper Colorado River Basin, the Yampa and the Upper-Green rivers. I used these data to identify forcing functions governing streamflow. Forcing functions include environmental factors such as temperature and precipitation and anthropogenic factors such
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Fayad, Farah. "Apprentissage et annulation des bruits impulsifs sur un canal CPL indoor en vue d'améliorer la QoS des flux audiovisuels." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2012. http://tel.archives-ouvertes.fr/tel-00769953.

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Le travail présenté dans cette thèse a pour objectif de proposer et d'évaluer les performances de différentes techniques de suppression de bruit impulsif de type asynchrone adaptées aux transmissions sur courants porteurs en lignes (CPL) indoor. En effet, outre les caractéristiques physiques spécifiques à ce type de canal de transmission, le bruit impulsif asynchrone reste la contrainte sévère qui pénalise les systèmes CPL en terme de QoS. Pour remédier aux dégradations dues aux bruits impulsifs asynchrones, les techniques dites de retransmission sont souvent très utilisées. Bien qu'elles soie
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Book chapters on the topic "Empirical mode decomposition (EMD)"

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Masuda, Makoto, and Taichi Shiiba. "Temporal and Frequency Analysis with Empirical Mode Decomposition for Vehicle Vibration Signals." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_67.

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AbstractThis study presents the empirical mode decomposition method (EMD) for vehicle vibration and a correlation detection approach for input from road and vehicle body vibration using the Hilbert-Huang transform (HHT). Although the magnitude squared coherence is commonly used to examine the correlation of vehicle vibration with road input, it is not suitable for non-stationary vibration. On the other hand, the Hilbert-Huang transform (HHT) which consists of EMD and the Hilbert transform is proposed. This method is suitable for transient vibration analysis, while the drawbacks to intermittenc
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Labate, Domenico, Fabio La Foresta, Giuseppe Morabito, Isabella Palamara, and Francesco Carlo Morabito. "On the Use of Empirical Mode Decomposition (EMD) for Alzheimer’s Disease Diagnosis." In Advances in Neural Networks: Computational and Theoretical Issues. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18164-6_12.

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Wang, Heming, Richard Mann, and Edward R. Vrscay. "A Diffusion-Based Two-Dimensional Empirical Mode Decomposition (EMD) Algorithm for Image Analysis." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93000-8_34.

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Jatia, Nikita, and Karan Veer. "Performance Comparison of Denoising Methods for Fetal Phonocardiography Using Fir Filter and Empirical Mode Decomposition (EMD)." In Optimization Methods for Engineering Problems. Apple Academic Press, 2023. http://dx.doi.org/10.1201/9781003300731-12.

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Tabrizi, A., L. Garibaldi, A. Fasana, and S. Marchesiello. "Influence of Stopping Criterion for Sifting Process of Empirical Mode Decomposition (EMD) on Roller Bearing Fault Diagnosis." In Lecture Notes in Mechanical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39348-8_33.

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Chang, H. C., P. L. Lee, and C. H. Wu. "Empirical Mode Decomposition (EMD) – Based Spatiotemporal Approach for Single-Trial Extraction of Post-Movement MEG Beta Synchronization." In IFMBE Proceedings. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03882-2_290.

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Srivastava, Ashita, Vikrant Bhateja, Deepak Kumar Tiwari, and Deeksha Anand. "AWGN Suppression Algorithm in EMG Signals Using Ensemble Empirical Mode Decomposition." In Intelligent Computing and Information and Communication. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7245-1_50.

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Wang, Yuchen. "Real-Time Tsunami Detection Based on Ensemble Empirical Mode Decomposition (EEMD)." In Springer Theses. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-7339-0_4.

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Fatmawati, T. Y., A. Yuliani, M. A. Afandi, and D. Zulherman. "Comparative Analysis of the Phonocardiogram Denoising System Based-on Empirical Mode Decomposition (EMD) and Double-Density Discrete Wavelet Transform (DDDWT)." In Proceedings of the 1st International Conference on Electronics, Biomedical Engineering, and Health Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6926-9_52.

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Panda, Saroj Kumar, Papia Ray, and Debani Prasad Mishra. "Short Term Load Forecasting Using Empirical Mode Decomposition (EMD), Particle Swarm Optimization (PSO) and Adaptive Network-Based Fuzzy Interference Systems (ANFIS)." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49339-4_17.

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Conference papers on the topic "Empirical mode decomposition (EMD)"

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Das, Daisy, Nitin Choudhury, Nabamita Deb, Vaskar Deka, Manoj Kumar Deka, and Shikhar Kumar Sarma. "Enhanced Signal Decomposition Using Golay-Optimized Empirical Mode Decomposition (Go-EMD)." In 2024 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET). IEEE, 2024. https://doi.org/10.1109/icrpset64863.2024.10955941.

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Zhao, Yong-tao, and Xing-peng Guo. "Time-Frequency Signal De-Noising of Coulostatically -Induced Transients Based on Empirical Mode Decomposition." In CORROSION 2007. NACE International, 2007. https://doi.org/10.5006/c2007-07458.

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Abstract A method of de-noising based on empirical mode decomposition (EMD) for coulostatically-induced transients (CITs) is presented. This filtering was illustrated with CIT and its associated impedance spectra, and comparisons have been made with wavelet de-noising and FIR method. The results reported here demonstrate that EMD de-noising is applicable to coulostatic measurements data in both the time and frequency domains, where wavelet filtering can also be successful. Moreover, in cases where the non-linear CITs need de-noise, EMD can eliminate noisy data from CIT. The proposed method sho
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Shi, Jiaxin, Jun Hu, Huapeng Zhao, Wei Chen, Min Zhang, and Zhiwei Gao. "Analyzing Transient Phenomena in the Time Domain Using Empirical Mode Decomposition(EMD)." In 2024 IEEE 12th Asia-Pacific Conference on Antennas and Propagation (APCAP). IEEE, 2024. https://doi.org/10.1109/apcap62011.2024.10881315.

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Tang, Fujian, Lizhi Zhao, Hong-Nan Li, et al. "Spatial Variation Analysis of Localized Corrosion of Steel Bar with Spectral Analysis Technique." In CONFERENCE 2022. AMPP, 2022. https://doi.org/10.5006/c2022-17792.

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Abstract This study aims to statistically analyze the distribution characteristics of localized corrosion along the length of corroded steel bars with spectral analysis techniques. Steel bars were embedded in a concrete prism and subjected to accelerated corrosion to levels ranging from 5.0 wt.% to 30.0 wt.% mass loss. After the corrosion test, the corroded steel bars were taken out of the concrete and cleaned with a sand blaster, and then scanned with a 3D laser scanner. The scanned point clouds were processed with an image processing software to determine the residual cross-sectional area di
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Alam, MD Erfanul, and Biswanath Samanta. "Performance Evaluation of Empirical Mode Decomposition for EEG Artifact Removal." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71647.

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Electroencephalography measures the sum of the post-synaptic potentials generated by many neurons having the same radial orientation with respect to the scalp. The electroen-cephalographic signals (EEG) are weak and often contaminated with different artifacts that have biological and external sources. Reliable pre-processing of the noisy, non-linear, and non-stationary brain activity signals is needed for successful extraction of characteristic features in motor imagery based brain-computer interface (MI-BCI). In this work, a signal processing technique, namely, empirical mode decomposition (E
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Fan, Xianfeng, and Ming J. Zuo. "Gearbox Fault Detection Using Empirical Mode Decomposition." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59349.

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Local faults in a gearbox cause impacts and the collected vibration signal is often non-stationary. Identification of impulses within the non-stationary vibration signal is key to fault detection. Recently, the technique of Empirical Mode Decomposition (EMD) was proposed as a new tool for analysis of non-stationary signal. EMD is a time series analysis method that extracts a custom set of bases that reflects the characteristic response of a system. The Intrinsic Mode Functions (IMFs) within the original data can be obtained through EMD. We expect that the change in the amplitude of the special
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Zhang, Faping, Jibin Yang, Tiguang Zhang, and Yan Yan. "Surface Topography Separation Based on Wavelet Reconstruction and Empirical Mode Decomposition." In ASME 2015 International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/msec2015-9310.

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Surface topography decomposition used to identify the components of the machined surface is an important method for tracing machining errors and predicting surface performance. This paper presents a generalized and practical method to fulfill the surface topography decomposition by wavelet reconstruction and Empirical Mode Decomposition (EMD). Firstly, by choosing suitable base function, wavelet transformation technique was applied for surface decomposition to get surface components in different level, with some levels containing uncertain information, indicating that wavelet transform was not
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Morishita, Helio Mitio, Leonardo Kubota, Michaelli Sforsin Vestri, Solenn Greuell, and La´zaro Moratelli. "The Empirical Mode Decomposition Applied to Dynamic Positioning Systems." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-50025.

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Dynamic Positioning Systems installed in marine vessels require a filtering block to attenuate the wave frequency components captured by the position and heading sensors. In this paper a filtering algorithm based on the Empirical Mode Decomposition (EMD) is suggested. The EMD is an algorithm that claims to properly identify the time scales featuring the physical characteristics of any given system by analyzing a time series representative of the system dynamics. It can handle both non stationary and non linear signals and it requires no previous knowledge on the system dynamics. Details of the
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Shahbakhti, Mohammad, Vahidreza Khalili, and Golnoosh Kamaee. "Removal of blink from EEG by Empirical Mode Decomposition (EMD)." In 2012 5th Biomedical Engineering International Conference (BMEiCON). IEEE, 2012. http://dx.doi.org/10.1109/bmeicon.2012.6465451.

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Djeddou, Messaoud, Xingang Zhao, Ibrahim A. Hameed, and Ahmed Rahmani. "Hybrid Improved Empirical Mode Decomposition and Artificial Neural Network Model for the Prediction of Critical Heat Flux (CHF)." In 2021 28th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/icone28-64879.

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Abstract Three Hybrid artificial neural network (ANN) models namely radial basis function (RBF), generalized regression neural networks (GRNN), and multi-layer perceptron (MLP) combined with empirical mode decomposition (EMD) are developed for CHF predictive modelling using CHF experimental databases. First, the original experimental inputs data series are decomposed into several intrinsic mode functions (IMFs) and one residual by EMD, whose components are divided into high, medium and low components. The performance parameters of the hybrid models indicates that the root mean square error (RM
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Reports on the topic "Empirical mode decomposition (EMD)"

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Khatri, Hiralal, Kenneth Ranney, Kwok Tom, and Romeo del Rosario. Empirical Mode Decomposition Based Features for Diagnosis and Prognostics of Systems. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada486738.

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Zhou, Feng, Lijun Yang, Hao-min Zhou, and Lihua Yang. Optimal Averages for Nonlinear Signal Decompositions - Another Alternative for Empirical Mode Decomposition. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada610276.

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Huang, Norden E. The Application of the Empirical Mode Decomposition and Hilbert Spectral Analysis to Field Data and Future Experimental Designs. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada627728.

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Huang, Norden E. Development a Statistical Measure for Empirical Mode Decomposition and Hilbert Spectral Analysis and its Applications to Oceanographic Data. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada634054.

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Huang, Norden E. The Application of the Empirical Mode Decomposition and Hilbert Spectral Analysis to Field Data and Future Experimental Designs. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada636671.

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Debuque-Gonzales, Margarita, Charlotte Justine Diokno-Sicat, John Paul Corpus, Robert Hector Palomar, Mark Gerald Ruiz, and Ramona Maria Miral. Fiscal Effects of the COVID-19 Pandemic: Assessing Public Debt Sustainability in the Philippines. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.17.

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Abstract:
This paper examines whether the current level of debt in the country, given the national government’s fiscal policy and plans, remains on a sustainable path. By end-2021, a year after the peak of the public health and economic crisis brought about by the COVID-19 pandemic, the country’s debt-to-GDP ratio had already climbed to 60.5 percent, over 20 percentage points above pre-pandemic levels and slightly above the government’s indicative cap. Several empirical exercises were performed in this paper to investigate the country’s fiscal solvency, namely by (1) providing a historical decomposition
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