scholarly journals Time-varying system identification using variational mode decomposition

2018 ◽  
Vol 25 (6) ◽  
pp. e2175 ◽  
Author(s):  
Pinghe Ni ◽  
Jun Li ◽  
Hong Hao ◽  
Yong Xia ◽  
Xiangyu Wang ◽  
...  
Keyword(s):  
System Identification ◽  
Time Varying ◽  
Variational Mode Decomposition ◽  
Mode Decomposition

A Combined Method for Time-Varying Parameter Identification Based on Variational Mode Decomposition and Generalized Morse Wavelet

2020 ◽  
Vol 20 (07) ◽  
pp. 2050077
Author(s):  
Chao Wang ◽  
Jing Zhang ◽  
Hong Pin Zhu
Keyword(s):  
Parameter Identification ◽  
Structural Response ◽  
Time Varying ◽  
Variational Mode Decomposition ◽  
Combined Method ◽  
Moving Vehicle ◽  
Mode Decomposition ◽  
Time Varying Parameter ◽  
Time Varying Systems ◽  
And Performance

Time-varying parameter identification is essential for structural health monitoring and performance evaluation. In this paper, a combined method based on the variational mode decomposition and generalized Morse wavelet is proposed to identify the structural time-varying parameters. Based on the sparse property of structural response signals in wavelet domain, a fast iterative shrinkage-thresholding algorithm is adopted to reduce the noise. Then the de-noised signal is decomposed into multi- modes by the variational mode decomposition, and the generalized Morse wavelet is performed to identify the instantaneous frequency. To validate the proposed method, a numerical example including different frequency variations is studied. Experimental validations of a moving vehicle across a bridge and a time-varying cable system considering two patterns of cable tension variations in the laboratory are carried out to investigate the capability of the proposed approach. It is confirmed that the proposed approach can effectively perform the signal decomposition, while identifying the instantaneous frequencies of the time-varying systems accurately.

scholarly journals Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables

2021 ◽  
Vol 11 (3) ◽  
pp. 1254
Author(s):  
Shitong Hou ◽  
Bin Dong ◽  
Jianhua Fan ◽  
Gang Wu ◽  
Haochen Wang ◽  
...  
Keyword(s):  
Good Effect ◽  
Time Varying ◽  
Variational Mode Decomposition ◽  
Stay Cable ◽  
Cable Tension ◽  
Force Identification ◽  
Stay Cables ◽  
Mode Decomposition ◽  
Cable Force ◽  
Cable Stayed Bridges

Stay cables are important structural members of cable-stayed bridges, which play a significant role in the health monitoring and assessment of cable-stayed bridges. The in-service cable force, which varies from the effects of vehicle load, wind load and other environmental factors, may cause fatigue damage in stay cables. Traditional force identification methods can only calculate the time-average cable force instead of the instantaneous force. A novel method has been proposed in this paper for identifying time-varying cable tension based on the variational mode decomposition (VMD) method. This recent method decomposes signals and adaptively estimates instantaneous frequency combined with the Hilbert–Huang transform method. In the proposed study, the time-varying modal frequencies were identified from stay cable acceleration data, and then the time-varying cable tension was identified by the relationship between cable tension and identified fundamental frequency. Scaled and full-scale models of stay cables were implemented successively to illustrate the validity of the proposed method. The results showed that the variational mode decomposition (VMD) method has a good effect on identifying the time-varying cable forces, even the sudden changes in cable force. According to the cable force identification results, the maximum error was 8.4%, which meets the actual application of time-varying cable force measurements. An on-site test was also implemented to monitor the cable force during a construction period, and the results showed that the proposed method can provide accurate real-time results for evaluation and decision-making.

scholarly journals Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1952
Author(s):  
May Phu Paing ◽  
Supan Tungjitkusolmun ◽  
Toan Huy Bui ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Deep Learning ◽  
Brain Infarction ◽  
Three Dimensional ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Variational Mode Decomposition ◽  
Brain Scans ◽  
Automated Method ◽  
Mode Decomposition ◽  
Segmentation Task

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

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