A Denoising Method for Ship-Radiated Noise Based on Optimized Variational Mode Decomposition with Snake Optimization and Dual-Threshold Criteria of Correlation Coefficient

Li, Yuxing; Xiao, Luqi; Tang, Bingzhao; Liang, Lili; Lou, Yilan; Guo, Xinyao; Xue, Xiaohui

doi:10.1155/2022/8024753

Cited by 13 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the snake optimization algorithm has shown very good results in coupling work with other algorithms, such as noise denoising, gas outburst prediction, etc. [31,32].…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic Signal Timing Optimization Model Based on Video Surveillance Data and Snake Optimization Algorithm

Cheng,

Qiao,

et al. 2023

Sensors

View full text Add to dashboard Cite

With the continued rapid growth of urban areas, problems such as traffic congestion and environmental pollution have become increasingly common. Alleviating these problems involves addressing signal timing optimization and control, which are critical components of urban traffic management. In this paper, a VISSIM simulation-based traffic signal timing optimization model is proposed with the aim of addressing these urban traffic congestion issues. The proposed model uses the YOLO-X model to obtain road information from video surveillance data and predicts future traffic flow using the long short-term memory (LSTM) model. The model was optimized using the snake optimization (SO) algorithm. The effectiveness of the model was verified by applying this method through an empirical example, which shows that the model can provide an improved signal timing scheme compared to the fixed timing scheme, with a decrease of 23.34% in the current period. This study provides a feasible approach for the research of signal timing optimization processes.

show abstract

“…Moreover, the snake optimization algorithm has shown very good results in coupling work with other algorithms, such as noise denoising, gas outburst prediction, etc. [31,32].…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic Signal Timing Optimization Model Based on Video Surveillance Data and Snake Optimization Algorithm

Cheng,

Qiao,

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Li and Wang [15] used three decomposition methods to preprocess raw signals and removed the noise by using the minimum mean square variance criterion. Li et al [16] used the snake optimization with envelope entropy to optimize the decomposition number and penalty in the VMD and then used the dual-threshold criteria of correlation coefficient to remove the noise. However, it is not applicable to noise reduction under low excitation voltages.…”

Section: Related Workmentioning

confidence: 99%

Reconstructing echoes completely submerged in background noise by a stacked denoising autoencoder method for low-power EMAT testing

Zhou,

Yu,

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Low-power electromagnetic-acoustic transducer (EMAT) is crucially important for safety-critical equipment in industry, especially for potential explosives and inflammable petrochemical equipment and facilities. When the excitation power is very low, the corresponding echoes are overwhelmed in noise and related measurement would be inaccurate. To solve this problem, this paper presents a new echo reconstruction method based on a deep stacked denoising autoencoder (DSDAE) for nondestructive evaluation. First, the uses of reference signals and new data structure are to improve the training efficiency. A hybrid method based on variational mode decomposition and wavelet transform is used to obtain clean reference signals as inputs of the deep network. Then, the modified network structure and loss function aim to improve the ability of feature extraction and reconstruct clean echoes from low-power EMAT signals. To validate the effectiveness of the proposed method, the experiments of self-excitation and receiving A-scan inspections of stepped specimens with different thicknesses are conducted at some excitation voltages, as low as 25V. The results indicate that the proposed DSDAE shows better and more stable denoising performances than some popular processing methods for different specimens and excitation voltages. It greatly improves the signal-to-noise ratio (SNR) of the reconstructed signal to 20dB. When applying to thickness measurement of specimens, its relative error is lower than 0.3%, which provides a practical and accurate tool for low-power EMAT testing.

show abstract

“…Changing the topology to design update strategies tailored to particles with distinct characteristics can optimize the utilization of information within the particle swarm 15 . Liang introduced the APSO-C algorithm 16 , which incorporates two key strategies.…”

Section: Introductionmentioning

confidence: 99%

Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems

Guo,

Zhou,

Yan

et al. 2023

Sci Rep

View full text Add to dashboard Cite

High-dimensional optimization presents a novel challenge within the realm of intelligent computing, necessitating innovative approaches. When tackling high-dimensional spaces, traditional evolutionary tools often encounter pitfalls, including dimensional catastrophes and a propensity to become trapped in local optima, ultimately compromising result accuracy. To address this issue, we introduce the Pair Barracuda Swarm Optimization (PBSO) algorithm in this paper. PBSO employs a unique strategy for constructing barracuda pairs, effectively mitigating the challenges posed by high dimensionality. Furthermore, we enhance global search capabilities by incorporating a support barracuda alongside the leading barracuda pair. To assess the algorithm’s performance, we conduct experiments utilizing the CEC2017 standard function and compare PBSO against five state-of-the-art natural-inspired optimizers in the control group. Across 29 test functions, PBSO consistently secures top rankings with 9 first-place, 13 second-place, 5 third-place, 1 fourth-place, and 1 fifth-place finishes, yielding an average rank of 2.0345. These empirical findings affirm that PBSO stands as the superior choice among all test algorithms, offering a dependable solution for high-dimensional optimization challenges.

show abstract

A Denoising Method for Ship-Radiated Noise Based on Optimized Variational Mode Decomposition with Snake Optimization and Dual-Threshold Criteria of Correlation Coefficient

Cited by 13 publications

References 20 publications

Traffic Signal Timing Optimization Model Based on Video Surveillance Data and Snake Optimization Algorithm

Traffic Signal Timing Optimization Model Based on Video Surveillance Data and Snake Optimization Algorithm

Reconstructing echoes completely submerged in background noise by a stacked denoising autoencoder method for low-power EMAT testing

Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems

Contact Info

Product

Resources

About