EIT Image Processing Based on 2-D Empirical Mode Decomposition

2012 ◽  
Vol 268-270 ◽  
pp. 1906-1909
Author(s):  
Qing He ◽  
Xiao Yan Chen
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Empirical Mode Decomposition ◽  
Basis Function ◽  
Small Volume ◽  
Impedance Tomography ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Adaptive Time

Electrical impedance tomography (EIT) imaging is a new kind of imaging technology that has developed in recent years. This technology has many advantages such as noninvasive, small volume and so on, but there are also many shortcomings such as imaging quality is not high, in order to improve the image quality, image processing methods is used in this paper. Significant different with the traditional method, empirical mode decomposition method does not require any prior basis function. It is an adaptive time-frequency analysis method. The two-dimensional empirical mode decomposition algorithm is adopted to process EIT images in this paper, and the compactly supported radial basis function interpolation algorithm is used for the post-processing of three organic glass rod EIT imaging. The image is decomposed into six IMFs in the experiment, some IMFs contain useful information, and the rest of them contains a lot of noise. The experimental results show that this method can significantly improve the EIT image quality.

scholarly journals Application of ensemble empirical mode decomposition based on machine learning methodologies in forecasting monthly pan evaporation

2018 ◽  
Vol 50 (2) ◽  
pp. 498-516 ◽  
Author(s):  
Mohammad Rezaie-Balf ◽  
Ozgur Kisi ◽  
Lloyd H. C. Chua
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Support Vector ◽  
Pan Evaporation ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Testing Stage ◽  
Adaptive Time ◽  
Two Hybrid

Abstract Accurate prediction of pan evaporation (PE) is one of the crucial factors in water resources management and planning in agriculture. In this research, two hybrid models, self-adaptive time-frequency methodology, ensemble empirical mode decomposition (EEMD) coupled with support vector machine (EEMD-SVM) and EEMD model tree (EEMD-MT), were employed to forecast monthly PE. The EEMD-SVM and EEMD-MT were compared with single SVM and MT models in forecasting monthly PE, measured between 1975 and 2008, at Siirt and Diyarbakir stations in Turkey. The results were evaluated using four assessment criteria, Nash–Sutcliffe Efficiency (NSE), root mean square error (RMSE), performance index (PI), Willmott's index (WI), and Legates–McCabe's index (LMI). The EEMD-MT model respectively improved the accuracy of MT by 36 and 44.7% with respect to NSE and WI in the testing stage for the Siirt station. For the Diyarbakir station, the improvements in results were less spectacular, with improvements in NSE (1.7%) and WI (2.2%), respectively, in the testing stage. The overall results indicate that the proposed pre-processing technique is very promising for complex time series forecasting and further studies incorporating this technique are recommended.

A Method of Fault Detection and Diagnosis Based on Time-Frequency Analysis

2012 ◽  
Vol 490-495 ◽  
pp. 1407-1410
Author(s):  
Ying Bo Liang ◽  
Li Hong Zhang ◽  
Jin Li
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Maximum Modulus ◽  
Fault Detection And Diagnosis ◽  
Time Frequency ◽  
Multi Scale ◽  
Mode Function ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Detection And Diagnosis

In the paper the authors propose a combination of the EMD (empirical mode decomposition)method and the wavelet analysis to suppress the noise and fault detection and diagnosis, It adopts empirical mode decomposition to current signal ,obtained a series of IMFs(Intrinsic Mode Function),removing the first IMF component to denosing,and then analyzed multi-scale ,using signal become mutated have the maximum modulus determine the time that the failure appeared ,the results show that this method determine the time that the failure appeared.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals Multivariate empirical mode decomposition–based structural damage localization using limited sensors

2021 ◽  
pp. 107754632110069
Author(s):  
Sandeep Sony ◽  
Ayan Sadhu
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Cost Effective ◽  
Threshold Value ◽  
Damage Localization ◽  
Time Frequency ◽  
Multivariate Empirical Mode Decomposition ◽  
Percentage Difference ◽  
Mode Decomposition

In this article, multivariate empirical mode decomposition is proposed for damage localization in structures using limited measurements. Multivariate empirical mode decomposition is first used to decompose the acceleration responses into their mono-component modal responses. The major contributing modal responses are then used to evaluate the modal energy for the respective modes. A damage localization feature is proposed by calculating the percentage difference in the modal energies of damaged and undamaged structures, followed by the determination of the threshold value of the feature. The feature of the specific sensor location exceeding the threshold value is finally used to identify the location of structural damage. The proposed method is validated using a suite of numerical and full-scale studies. The validation is further explored using various limited measurement cases for evaluating the feasibility of using a fewer number of sensors to enable cost-effective structural health monitoring. The results show the capability of the proposed method in identifying as minimal as 2% change in global modal parameters of structures, outperforming the existing time–frequency methods to delineate such minor global damage.

Frequency decomposition and phase synchronization of the visual evoked potential using the empirical mode decomposition

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Byuckjin Lee ◽  
Byeongnam Kim ◽  
Sun K. Yoo
Keyword(s):  
Empirical Mode Decomposition ◽  
Brain Function ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Posterior Lobe ◽  
Time Frequency ◽  
Brain Functions ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Visual Evoked

AbstractObjectivesThe phase characteristics of the representative frequency components of the Electroencephalogram (EEG) can be a means of understanding the brain functions of human senses and perception. In this paper, we found out that visual evoked potential (VEP) is composed of the dominant multi-band component signals of the EEG through the experiment.MethodsWe analyzed the characteristics of VEP based on the theory that brain evoked potentials can be decomposed into phase synchronized signals. In order to decompose the EEG signal into across each frequency component signals, we extracted the signals in the time-frequency domain with high resolution using the empirical mode decomposition method. We applied the Hilbert transform (HT) to extract the signal and synthesized it into a frequency band signal representing VEP components. VEP could be decomposed into phase synchronized δ, θ, α, and β frequency signals. We investigated the features of visual brain function by analyzing the amplitude and latency of the decomposed signals in phase synchronized with the VEP and the phase-locking value (PLV) between brain regions.ResultsIn response to visual stimulation, PLV values were higher in the posterior lobe region than in the anterior lobe. In the occipital region, the PLV value of theta band was observed high.ConclusionsThe VEP signals decomposed into constituent frequency components through phase analysis can be used as a method of analyzing the relationship between activated signals and brain function related to visual stimuli.

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