scholarly journals Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3133
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Ryan Wen Liu ◽  
Pandian Vasant
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Ensemble Empirical Mode Decomposition ◽  
Wavelet Packet Transform ◽  
Smart Meters ◽  
Model Decomposition ◽  
Mode Decomposition ◽  
Data Heterogeneity ◽  
Electricity Load ◽  
Load Disaggregation

Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD–WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD–WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly.

scholarly journals A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 597 ◽  
Author(s):  
Guohui Li ◽  
Zhichao Yang ◽  
Hong Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Measurement Technology ◽  
Signal Denoising ◽  
Frequency Noise ◽  
Denoising Method ◽  
Radiated Noise ◽  
Mode Decomposition ◽  
Adaptive Noise

Due to the non-linear and non-stationary characteristics of ship radiated noise (SR-N) signal, the traditional linear and frequency-domain denoising methods cannot be used for such signals. In this paper, an SR-N signal denoising method based on modified complete ensemble empirical mode decomposition (EMD) with adaptive noise (CEEMDAN), dispersion entropy (DE), and interval thresholding is proposed. The proposed denoising method has the following advantages: (1) as an improved version of CEEMDAN, modified CEEMDAN (MCEEMDAN) combines the advantages of EMD and CEEMDAN, and it is more reliable than CEEMDAN and has less consuming time; (2) as a fast complexity measurement technology, DE can effectively identify the type of intrinsic mode function (IMF); and (3) interval thresholding is used for SR-N signal denoising, which avoids loss of amplitude information compared with traditional denoising methods. Firstly, the original signal is decomposed into a series of IMFs using MCEEMDAN. According to the DE value of IMF, the modes are divided into three types: noise IMF, noise-dominated IMF and pure IMF. After noise IMFs are removed, the noise-dominated IMFs are denoised using interval thresholding. Finally, the pure IMF and the processed noise-dominated IMFs are reconstructed to obtain the final denoised signal. The denoising experiments with the Chen’s chaotic system show that the proposed method has a higher signal-to-noise ratio (SNR) than the other three methods. Applying the proposed method to denoise the real SR-N signal, the topological structure of chaotic attractor can be recovered clearly. It is proved that the proposed method can effectively suppress the high-frequency noise of SR-N signal.

Dual-Tree Wavelet Packet Transform for Ultrasonic Data Transmission Through Metal Structures

2015 ◽  
Author(s):  
Jinjiang Wang ◽  
Robert X. Gao ◽  
Xinyao Tang ◽  
Zhaoyan Fan ◽  
Peng Wang
Keyword(s):  
Signal Processing ◽  
Data Transmission ◽  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Experimental Studies ◽  
Wavelet Packet Transform ◽  
Signal To Noise ◽  
Metallic Structures ◽  
Time Frequency ◽  
Noise Ratio

Data communication through metallic structures is generally encountered in manufacturing equipment and process monitoring and control. This paper presents a signal processing technique for enhancing the signal-to-noise ratio and high-bit data transmission rate in ultrasound-based wireless data transmission through metallic structures. A multi-carrier coded-ultrasonic wave modulation scheme is firstly investigated to achieve high-bit data rate communication while reducing inter-symbol inference and data loss, due to the inherent signal attenuation, wave diffraction and reflection in metallic structures. To improve the signal-to-noise ratio, dual-tree wavelet packet transform (DT-WPT) has been investigated to separate multi-carrier signals under noise contamination, given its properties of shift-invariance and flexible time frequency partitioning. A new envelope extraction and threshold setting strategy for selected wavelet coefficients is then introduced to retrieve the coded digital information. Experimental studies are performed to evaluate the effectiveness of the developed signal processing method for manufacturing.

scholarly journals EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach

2021 ◽  
Author(s):  
Chamandeep Kaur ◽  
◽  
Preeti Singh ◽  
Sukhtej Sahni ◽  
◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Mode Selection ◽  
Classification Performance ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Analysis Tool ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Mixing Problem

Introduction: A number of computer- aided diagnosis systems for depression are being offered to be used by the clinicians as a method to authorize the diagnosis. EEG may be used as an objective analysis tool for identification of depression in the initial stage so as to avoid it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a novel denoising method based on EMD (Empirical Mode Decomposition) with detrended fluctuation analysis (DFA) and wavelet packet transform. As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying EMD. Then, DFA is used as the mode selection criteria. Further wavelet packets decomposition (WPD) based evaluation is used to extract the cleaner signal. Results: Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. To conclude the efficacy of the proposed technique, SNR and MAE have been identified. The results show improved signal to noise ratio and lower values of MAE for the combined EMD-DFA-WPD technique. Also, Random Forest and SVM (Support Vector Machine) based classification shows improved accuracy of 98.51% and 98.10% for the proposed denoising technique. Whereas the accuracy of the EMD- DFA is 98.01% and 95.81% and EMD combined with DWT technique is 98.0% and 97.21% for the EMD- DFA technique for RF and SVM respectively as compared to the proposed method. Also, the classification performance for both the classifiers has been compared with and without denoising to highlight the effectiveness of the proposed technique. Conclusion: Proposed denoising system results in better classification of depressed and healthy individuals resulting in better diagnosing system. These results can be further analyzed using other approaches as a solution to the mode mixing problem of EMD approach.

scholarly journals Design of sEMG Acquisition Circuit and Its Adaptive EEMD Denosing Research

2022 ◽  
Vol 16 ◽  
pp. 509-516
Author(s):  
Wei Li ◽  
Wei Hu ◽  
Kun Hu ◽  
Qiang Qin
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
Electrical Signal ◽  
Signal Acquisition ◽  
Semg Signal ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Human Muscles ◽  
Eemd Method

The Surface electromyography (sEMG) signal is a kind of electrical signal which generated by human muscles during contraction. It is prone to being affected by noise because of its small amplitude, so it is necessary to remove the noise in its original signal with an appropriate algorithm. Based on the traditional signal denoising indicators, a new complex indicator r has been proposed in this paper which combines three different indicator parameters, that is, Signal to Noise Ratio (SNR), correlation coefficient (R), and standard error (SE). At the same time, an adaptive ensemble empirical mode decomposition (EEMD) method named AIO-EEMD which based on the proposed indicator is represented later. To verify the effective of the proposed algorithm, an electromyography signal acquisition circuit is designed firstly for collecting the original sEMG signal. Then, the denosing performance from the designed method is been compared with empirical mode decomposition (EMD) method and wavelet transform noise reduction method, respectively. The experiment results shown that the designed algorithm can not only automatically get the numbers of the reconstructed signal numbers, but also obtain the best reduction performance.

scholarly journals Multi-Fault Diagnosis of Gearbox Based on Improved Multipoint Optimal Minimum Entropy Deconvolution

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 611 ◽  
Author(s):  
Fuhe Yang ◽  
Xingquan Shen ◽  
Zhijian Wang
Keyword(s):  
White Noise ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Minimum Entropy ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Fault Features ◽  
Selection Of

Under complicated conditions, the extraction of a multi-fault in gearboxes is difficult to achieve. Due to improper selection of methods, leakage diagnosis or misdiagnosis will usually occur. Ensemble Empirical Mode Decomposition (EEMD) often causes energy leakage due to improper selection of white noise during signal decomposition. Considering that only a single fault cycle can be extracted when MOMED (Multipoint Optimal Minimum Entropy Deconvolution) is used, it is necessary to perform the sub-band processing of the compound fault signal. This paper presents an adaptive gearbox multi-fault-feature extraction method based on Improved MOMED (IMOMED). Firstly, EEMD decomposes the signal adaptively and selects the intrinsic mode functions with strong correlation with the original signal to perform FFT (Fast Fourier transform); considering the mode-mixing phenomenon of EEMD, reconstruct the intrinsic mode functions with the same timescale, and obtain several intrinsic mode functions of the same scale to improve the entropy of fault features. There is a lot of white noise in the original signal, and EEMD can improve the signal-to-noise ratio of the original signal. Finally, through the setting of different noise-reduction intervals to extract fault features through MOMED. The proposed method is compared with EEMD and VMD (Variational Mode Decomposition) to verify its feasibility.

scholarly journals Evaluating The Performance of Several Data Preprocessing Methods Based On GRU in Forecasting Monthly Runoff Time Series

2021 ◽  
Author(s):  
Wenchuan Wang ◽  
Yu-jin Du ◽  
Kwok-wing Chau ◽  
Chun-Tian Cheng ◽  
Dong-mei Xu ◽  
...  
Keyword(s):  
Time Series ◽  
Performance Metrics ◽  
Wavelet Packet ◽  
Singular Spectrum Analysis ◽  
Data Preprocessing ◽  
Ensemble Empirical Mode Decomposition ◽  
Runoff Forecasting ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Monthly Runoff

Abstract The optimal planning and management of modern water resources depends highly on reliable and accurate runoff forecasting. Data preprocessing technology can provide new possibilities for improving the accuracy of runoff forecasting, when basic physical relationships cannot be captured using a single prediction model. Yet, few researches evaluated the performances of various data preprocessing technology in predicting monthly runoff time series so far. In order to fill this research gap, this paper investigates the potential of five data preprocessing techniques based on gated recurrent unit network (GRU) model in monthly runoff prediction, namely variational mode decomposition (VMD), wavelet packet decomposition (WPD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), extreme-point symmetric mode decomposition (ESMD), and singular spectrum analysis (SSA). In this study, the original monthly runoff data is first decomposed into a set of subcomponents using five data preprocessing methods; second, each component is predicted by developing an appropriate GRU model; finally, the forecasting results of different two-stage hybrid models are obtained by aggregating of forecast results of the corresponding subcomponents. Four performance metrics are employed as the evaluation benchmarks. The experimental results from two hydropower stations in China show that five data preprocessing techniques can attain satisfying prediction results, while VMD and WPD methods can yield better performance than CEEMDAN, ESMD and SSA in both training and testing periods in terms of four indexes. Indeed, it is significantly important to carefully determine an appropriate data preprocessing method according to the actual characteristics of the study area.

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