scholarly journals Denoising GPS-Based Structure Monitoring Data Using Hybrid EMD and Wavelet Packet

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Lu Ke
Keyword(s):  
High Frequency ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Monitoring Data ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Structure Monitoring ◽  
Energy Leakage ◽  
Frequency Components ◽  
Different Characteristics

High-frequency components are often discarded for data denoising when applying pure wavelet multiscale or empirical mode decomposition (EMD) based approaches. Instead, they may raise the problem of energy leakage in vibration signals. Hybrid EMD and wavelet packet (EMD-WP) is proposed to denoise Global Positioning System- (GPS-) based structure monitoring data. First, field observables are decomposed into a collection of intrinsic mode functions (IMFs) with different characteristics. Second, high-frequency IMFs are denoised using the wavelet packet; then the monitoring data are reconstructed using the denoised IMFs together with the remaining low-frequency IMFs. Our algorithm is demonstrated on a synthetic displacement response of a 3-story frame excited by El Centro earthquake along with a set of Gaussian random white noises on different levels added. We find that the hybrid method can effectively weaken the multipath effect with low frequency and can potentially extract vibration feature. However, false modals may still exist by the rest of the noise contained in the high-frequency IMFs and when the frequency of the noise is located in the same band as that of effective vibration. Finally, real GPS observables are implemented to evaluate the efficiency of EMD-WP method in mitigating low-frequency multipath.

scholarly journals Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Gang Zhang ◽  
Hongchi Liu ◽  
Pingli Li ◽  
Meng Li ◽  
Qiang He ◽  
...  
Keyword(s):  
Power System ◽  
High Frequency ◽  
Prediction Accuracy ◽  
Load Forecasting ◽  
Low Frequency ◽  
Load Prediction ◽  
System Load ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Feature Information

Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.

Random Noise Reduction Based on Ensemble Empirical Mode Decomposition and Wavelet Threshold Filtering

2012 ◽  
Vol 518-523 ◽  
pp. 3887-3890 ◽  
Author(s):  
Wei Chen ◽  
Shang Xu Wang ◽  
Xiao Yu Chuai ◽  
Zhen Zhang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Reduction Method ◽  
Random Noise ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Eemd Method

This paper presents a random noise reduction method based on ensemble empirical mode decomposition (EEMD) and wavelet threshold filtering. Firstly, we have conducted spectrum analysis and analyzed the frequency band range of effective signals and noise. Secondly, we make use of EEMD method on seismic signals to obtain intrinsic mode functions (IMFs) of each trace. Then, wavelet threshold noise reduction method is used on the high frequency IMFs of each trace to obtain new high frequency IMFs. Finally, reconstruct the desired signal by adding the new high frequency IMFs on the low frequency IMFs and the trend item together. When applying our method on synthetic seismic record and field data we can get good results.

Frequency Modulated Empirical Mode Decomposition Method

2012 ◽  
Vol 433-440 ◽  
pp. 4776-4781
Author(s):  
Xin Zhang ◽  
Xiu Li Du
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Low Pass ◽  
Frequency Components ◽  
Band Pass ◽  
Stiffness And Damping ◽  
Mixing Problem

Frequency modulation procedure is proposed to overcome the mode-mixing problem associated with the EMD method when processing signals with closely spaced frequencies. This procedure also provides the flexibility to start the realization of IMFs either from the high frequency end as does the original EMD or from the low frequency end when the signal contains unwanted high frequency components. The EMD procedure, under the circumstances, may behave as high pass, low pass or band pass/stop filters. The proposed method, assisted by the Hilbert-Huang transform on the governing equations, identifies the instantaneous stiffness and damping coefficients as functions of vibration amplitude of a nonlinear system. The effectiveness of the proposed method is verified by numerical simulation.

ASSESSING DISCONTINUOUS DATA USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION

2011 ◽  
Vol 03 (04) ◽  
pp. 483-491 ◽  
Author(s):  
BRADLEY LEE BARNHART ◽  
HONDA KAHINDO WA NANDAGE ◽  
WILLIAM EICHINGER
Keyword(s):  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Sets ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Data Gaps ◽  
Discontinuous Data ◽  
Periodic Components

This investigation presents an improved ensemble empirical mode decomposition (EEMD) algorithm that can be applied to discontinuous data. The quality of the algorithm is assessed by creating artificial data gaps in continuous data, then comparing the extracted intrinsic mode functions (IMFs) from both data sets. The results show that errors increase as the gap length increases. In addition, errors in the high-frequency IMFs are less than the low-frequency IMFs. The majority of the errors in the high-frequency IMFs are due to end-effect errors associated with under-defined interpolation functions near the gap endpoints. A method that utilizes a mirroring technique is presented to reduce the errors in the discontinuous decomposition. The improved algorithm provides a more locally accurate decomposition of the data amidst data gaps. Overall, this simple but powerful algorithm expands EEMD's ability to locally extract periodic components from discontinuous data.

Simulations of high-resolution images of amorphous silicon films

1986 ◽  
Vol 44 ◽  
pp. 544-545
Author(s):  
G. Y. Fan ◽  
J. M. Cowley
Keyword(s):  
Electron Microscope ◽  
High Frequency ◽  
Fourier Transformation ◽  
Amorphous Materials ◽  
Low Frequency ◽  
Chromatic Aberration ◽  
Structure Information ◽  
Frequency Components ◽  
High Resolution Images ◽  
Spatial Frequency Spectrum

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.

A Novel Adaptive PET/CT Image Fusion Algorithm

2019 ◽  
Vol 14 (7) ◽  
pp. 658-666
Author(s):  
Kai-jian Xia ◽  
Jian-qiang Wang ◽  
Jian Cai
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
High Frequency ◽  
Malignant Tumors ◽  
Low Frequency ◽  
Combination Method ◽  
Ct Image ◽  
Fusion Algorithm ◽  
Pet Ct ◽  
Frequency Components

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

Despeckling of Sar Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

2020 ◽  
pp. 2150023
Author(s):  
Priya R. Kamath ◽  
Kedarnath Senapati ◽  
P. Jidesh
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Relevant Information ◽  
Detection Algorithm ◽  
Two Dimensional ◽  
Sar Images ◽  
S Transform ◽  
Processing Step ◽  
Frequency Components ◽  
Shock Filter

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

scholarly journals Image compression based on classification coding of constant-pitched functions transformers

2021 ◽  
pp. 48-62
Author(s):  
Vladimir Barannik ◽  
Andrii Krasnorutsky ◽  
Sergii Shulgin ◽  
Valerii Yeroshenko ◽  
Yevhenii Sidchenko ◽  
...  
Keyword(s):  
Image Compression ◽  
Data Transmission ◽  
High Frequency ◽  
Orthogonal Transformation ◽  
Low Frequency ◽  
Information Delivery ◽  
Video Data ◽  
Arithmetic Coding ◽  
Video Images ◽  
Frequency Components

The subject of research in the article are the processes of video image processing using an orthogonal transformation for data transmission in information and telecommunication networks. The aim is to build a method of compression of video images while maintaining the efficiency of its delivery at a given informative probability. That will allow to provide a gain in the time of delivery of compressed video images, a necessary level of availability and authenticity at transfer of video data with preservation of strictly statistical regulations and the controlled loss of quality. Task: to study the known algorithms for selective processing of static video at the stage of approximation and statistical coding of the data based on JPEG-platform. The methods used are algorithm based on JPEG-platform, methods of approximation by orthogonal transformation of information blocks, arithmetic coding. It is a solution of scientific task-developed methods for reducing the computational complexity of transformations (compression and decompression) of static video images in the equipment for processing visual information signals, which will increase the efficiency of information delivery.The following results were obtained. The method of video image compression with preservation of the efficiency of its delivery at the set informative probability is developed. That will allow to fulfill the set requirements at the preservation of structural-statistical economy, providing a gain in time to bring compressed images based on the developed method, relative to known methods, on average up to 2 times. This gain is because with a slight difference in the compression ratio of highly saturated images compared to the JPEG-2000 method, for the developed method, the processing time will be less by at least 34%.Moreover, with the increase in the volume of transmitted images and the data transmission speed in the communication channel - the gain in the time of delivery for the developed method will increase. Here, the loss of quality of the compressed/restored image does not exceed 2% by RMS, or not worse than 45 dB by PSNR. What is unnoticeable to the human eye.Conclusions. The scientific novelty of the obtained results is as follows: for the first time the method of classification (separate) coding (compression) of high-frequency and low-frequency components of Walsh transformants of video images is offered and investigated, which allows to consider their different dynamic range and statistical redundancy reduced using arithmetic coding. This method will allow to ensure the necessary level of availability and authenticity when transmitting video data, while maintaining strict statistical statistics.Note that the proposed method fulfills the set tasks to increase the efficiency of information delivery. Simultaneously, the method for reducing the time complexity of the conversion of highly saturated video images using their representation by the transformants of the discrete Walsh transformation was further developed. It is substantiated that the perspective direction of improvement of methods of image compression is the application of orthogonal transformations on the basis of integer piecewise-constant functions, and methods of integer arithmetic coding of values of transformant transformations.It is substantiated that the joint use of Walsh transformation and arithmetic coding, which reduces the time of compression and recovery of images; reduces additional statistical redundancy. To further increase the degree of compression, a classification coding of low-frequency and high-frequency components of Walsh transformants is developed. It is shown that an additional reduction in statistical redundancy in the arrays of low-frequency components of Walsh transformants is achieved due to their difference in representation. Recommendations for the parameters of the compression method for which the lowest value of the total time of information delivery is provided are substantiated.

scholarly journals Fusion of GNSS and Speedometer Based on VMD and Its Application in Bridge Deformation Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 694 ◽  
Author(s):  
Ruicheng Zhang ◽  
Chengfa Gao ◽  
Shuguo Pan ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
High Precision ◽  
High Frequency ◽  
Spectral Response ◽  
Low Frequency ◽  
Deformation Monitoring ◽  
Full Time ◽  
Dynamic Displacement ◽  
Mode Decomposition ◽  
Acceleration Sensors

Real-time dynamic displacement and spectral response on the midspan of Jiangyin Bridge were calculated using Global Navigation Satellite System (GNSS) and a speedometer for the purpose of understanding the dynamic behavior and the temporal evolution of the bridge structure. Considering that the GNSS measurement noise is large and the velocity/acceleration sensors cannot measure the low-frequency displacement, the Variational Mode Decomposition (VMD) algorithm was used to extract the low-frequency displacement of GNSS. Then, the low-frequency displacement extracted from the GNSS time series and the high-frequency vibration calculated by speedometer were combined in this paper in order to obtain the high precision three-dimensional dynamic displacement of the bridge in real time. Simulation experiment and measured data show that the VMD algorithm could effectively resist the modal aliasing caused by noise and discontinuous signals compared with the commonly used Empirical Mode Decomposition (EMD) algorithm, which is guaranteed to get high-precision fusion data. Finally, the fused displacement results can identify high-frequency vibrations and low-frequency displacements of a mm level, which can be used to calculate the spectral characteristics of the bridge and provide reference to evaluate the dynamic and static loads, and the health status of the bridge in the full frequency domain and the full time domain.

scholarly journals HHT based analysis on seismic response recordings for a base-isolated building

2019 ◽  
Vol 277 ◽  
pp. 02021
Author(s):  
Fei Wang ◽  
Xiandong Kang ◽  
Ting Yan ◽  
Ying Liu
Keyword(s):  
Seismic Response ◽  
Dominant Frequency ◽  
Data Series ◽  
Structural Systems ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Emd Method

Hilbert-Huang transform (HHT) is proposed to process the seismic response recordings in an 8-story frame-shear wall base-isolated building. Empirical Mode Decomposition (EMD) method is first applied to identify the time variant characteristics and the data series can be decomposed into several components. Hilbert transform is well-behaved in identifying the frequency components. The first 5 intrinsic mode functions (IMFs) are decomposed with their different frequencies. The analytical function is reconstructed and compared with the original signal. They are extremely consistent in amplitude and phase. Based on the IMFs obtained, frequencies of the original signal are inferred at 5 Hz and 1.6 Hz. The higher frequency is regarded as the vibration excited by surface waves. 1.6 Hz is suggested as the dominant frequency of the building. Analysis indicates that HHT is accurate in extracting the dynamic characteristics of structural systems.

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