scholarly journals MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode Decomposition and Correlation Coefficient

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4622 ◽  
Author(s):  
Huichao Yan ◽  
Ting Xu ◽  
Peng Wang ◽  
Linmei Zhang ◽  
Hongping Hu ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Whale Optimization Algorithm ◽  
Signal Denoising ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Baseline Drift ◽  
Mode Decomposition ◽  
Whale Optimization

Underwater acoustic technology is an important means of detecting the ocean. Due to the complex influence of the marine environment, there is a lot of noise and baseline drift in the signals collected by hydrophones. In order to solve this problem, this paper proposes a denoising and baseline drift removal algorithm for MEMS vector hydrophone based on whale-optimized variational mode decomposition (VMD) and correlation coefficient (CC). Firstly, the power spectrum entropy (PSE), which reflects the variation characteristics of the signal frequency is selected as the fitness function of the whale-optimization algorithm to find the parameters (K,α) of the VMD. It is easier to find the global optimal solution of the parameters by combining the whale-optimization algorithm. Then, using the VMD algorithm after obtaining the parameters, the original signal is decomposed to obtain the intrinsic mode functions (IMFs), and calculating the correlation coefficients (CCs) between the IMFs and the original signal. Finally, the CC threshold is used to remove the noise IMFs, and the rest of the useful IMFs are reconstructed to complete the denoising and baseline drift removal process of the original signals. In the simulation experiments, the algorithm proposed in this paper shows better performance by comparing conventional digital signal-processing methods and the related algorithms proposed recently. Applied in the experiments of a MEMS hydrophone, the effectiveness of the proposed algorithm is also verified. This algorithm can provide new ideas for signal denoising and baseline drift removal.

scholarly journals Efficient Lidar Signal Denoising Algorithm Using Variational Mode Decomposition Combined with a Whale Optimization Algorithm

2019 ◽  
Vol 11 (2) ◽  
pp. 126 ◽  
Author(s):  
Hongxu Li ◽  
Jianhua Chang ◽  
Fan Xu ◽  
Zhenxing Liu ◽  
Zhenbo Yang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Signal To Noise Ratio ◽  
Whale Optimization Algorithm ◽  
Variational Mode Decomposition ◽  
Bhattacharyya Distance ◽  
Detection Range ◽  
Sensing Technology ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Satisfactory Analysis

Although lidar is a powerful active remote sensing technology, lidar echo signals are easily contaminated by noise, particularly in strong background light, which severely affects the retrieval accuracy and the effective detection range of the lidar system. In this study, a coupled variational mode decomposition (VMD) and whale optimization algorithm (WOA) for noise reduction in lidar signals is proposed and demonstrated completely. The combination of optimal VMD parameters of decomposition mode number K and quadratic penalty α was obtained by using the WOA and was critical in acquiring satisfactory analysis results for VMD denoising technology. Then, the Bhattacharyya distance was applied to identify the relevant modes, which were reconstructed to achieve noise filtering. Simulation results show that the performance of the proposed VMD-WOA method is superior to that of wavelet transform, empirical mode decomposition, and its variations. Experimentally, this method was successfully used to filter a lidar echo signal. The signal-to-noise ratio of the denoised signal was increased to 23.92 dB, and the detection range was extended from 6 to 10 km.

scholarly journals Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Hongping Hu ◽  
Yan Ao ◽  
Huichao Yan ◽  
Yanping Bai ◽  
Na Shi
Keyword(s):  
Signal Processing ◽  
Hybrid Algorithm ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Signal Denoising ◽  
Original Signal ◽  
Variational Mode Decomposition ◽  
Swarm Optimization ◽  
Mode Decomposition ◽  
Vector Hydrophone

To eliminate the noise from the signals received by MEMS vector hydrophone, a joint algorithm is proposed in this paper based on wavelet threshold (WT) denoising, variational mode decomposition (VMD) optimized by a hybrid algorithm of Multiverse Optimizer (MVO) and Particle Swarm Optimization (PSO), and correlation coefficient (CC) judgment to perform the signal denoising of MEMS vector hydrophone, named as MVO-PSO-VMD-CC-WT, whose fitness function is the root mean square error (RMSE) and whose individual is the parameters of VMD. For every individual, the original signal is decomposed by VMD into pure components, noisy components, and noise components in terms of CC judgment, where the pure components are directly retained, the noisy components are denoised by WT denoising, and the noise components are discarded, and then, the denoised noisy components and the pure components are reconstructed to be the denoised signal of the original signal. Then, the obtained optimal individual is utilized to perform the signal denoising by MVO-PSO-VMD-CC-WT by the use of the above repeated signal processing. Two simulated experimental results show that the MVO-PSO-VMD-CC-WT algorithm which has the highest signal-to-noise ratio and the least RMSE is superior to the other compared algorithms. And the proposed MVO-PSO-VMD-CC-WT algorithm is effectively applied to perform the signal denoising of the actual lake experiments. Therefore, the proposed MVO-PSO-VMD-CC-WT is suitable for the signal denoising and can be applied into the actual experiments in signal processing.

A parameter estimation method for fractional-order nonlinear systems based on improved whale optimization algorithm

2019 ◽  
Vol 33 (07) ◽  
pp. 1950075 ◽  
Author(s):  
Gong Ren ◽  
Renhuan Yang ◽  
Renyu Yang ◽  
Pei Zhang ◽  
Xiuzeng Yang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Fractional Order ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Whale Optimization Algorithm ◽  
Engineering System ◽  
Grasshopper Optimization Algorithm ◽  
Whale Optimization ◽  
System Characteristics

Compared to the integer-order systems, the system characteristics of the fractional system are closer to the system characteristics of the real engineering system, the study found beyond that, strictly speaking, various physical phenomena in nature are nonlinear. The problem of parameter estimation problem of fractional-order nonlinear systems can be transformed into the problem of parameter optimization problem by constructing an appropriate fitness function. This paper proposes a hybrid improvement algorithm based on whale optimization algorithm (WOA) to solve this problem and verify it both in Lorenz system and Lu system. The simulation result shows that the hybrid improved algorithm is superior to genetic algorithm (GA), particle swarm optimization (PSO), grasshopper optimization algorithm (GOA) and WOA in convergence speed and accuracy.

scholarly journals Cloud-Assisted Privacy-Preserving Method for Healthcare Using Adaptive Fractional Brain Storm Integrated Whale Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
S. Thanga Revathi ◽  
A. Gayathri ◽  
J. Kalaivani ◽  
Mary Subaja Christo ◽  
Danilo Pelusi ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Fitness Function ◽  
Data Retrieval ◽  
Medical Data ◽  
Whale Optimization Algorithm ◽  
Experimental Result ◽  
Secret Key ◽  
Healthcare Data ◽  
Maximum Utility ◽  
Whale Optimization

The security of medical data in the cloud is the key consideration of cloud customers. While publishing the medical data, the cloud distributor may suffer from data leakages and attacks such that the data may leak. In order to resolve this, this article devises the developed Adaptive Fractional Brain Storm Integrated Whale Optimization Algorithm (AFBS_WOA), which is the hybridization of Adaptive Fractional Brain Storm Optimization (AFBSO) and Whale Optimization algorithm (WOA). The developed AFBS_WOA algorithm generates the key matrix coefficient for retrieving the perturbed database in order to preserve the privacy of healthcare data in the cloud. The developed AFBS-WOA scheme utilized the fitness function involving utility and privacy measures for calculating the secret key. Here, the privacy-preserved database is obtained by multiplying the input database with a key matrix based on developed AFBS-WOA using the Tracy–Singh product. For data retrieval, the secret key is shared with the service provider in order to retrieve the database, and then the data are accessed. Moreover, the experimental result demonstrates that the developed AFBS_WOA model attained the maximum utility and privacy measure of 0.1872 and 0.8755 using the Hungarian dataset.

Application of improved variational mode decomposition method based on two-dimensional sparrow search algorithm in natural gas pipeline leakage signal denoising

2021 ◽  
pp. 014233122110636
Author(s):  
Dongmei Wang ◽  
Lijuan Zhu ◽  
Jikang Yue ◽  
Jingyi Lu ◽  
Gongfa Li
Keyword(s):  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Decomposition Algorithm ◽  
Signal Denoising ◽  
Two Dimensional ◽  
Variational Mode Decomposition ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Whale Optimization

To eliminate noise interference in pipeline leakage detection, a signal denoising method based on an improved variational mode decomposition algorithm is proposed. This work adopts a standard variational mode decomposition algorithm with decomposition level K and the penalty factor α. The improvements consist of using a two-dimensional sparrow search algorithm to find K and α. To verify the superiority of the sparrow search algorithm to find K and α, it is compared with three earlier studies. These studies used the firefly algorithm, particle swarm optimization, and whale optimization algorithm to perform the optimization. The main result of this study is to demonstrate that the variational mode decomposition improved by sparrow search algorithm gives a much improved signal-to-noise ratio compared to the other methods. In all other respects, the results are comparable.

scholarly journals Multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast Multi-Output Relevance Vector Regression Optimized by the Multi-Objective Whale Optimization Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 147 ◽  
Author(s):  
Shenghua Xiong ◽  
Chunfeng Wang ◽  
Zhenming Fang ◽  
Dan Ma
Keyword(s):  
Optimization Algorithm ◽  
Carbon Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Variational Mode Decomposition ◽  
Forecasting Accuracy ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Relevance Vector Regression ◽  
Accuracy And Stability

The accurate and stable forecasting of carbon prices is vital for governors to make policies and essential for market participants to make investment decisions, which is important for promoting the development of carbon markets and reducing carbon emissions in China. However, it is challenging to improve the carbon price forecasting accuracy due to its non-linearity and non-stationary characteristics, especially in multi-step-ahead forecasting. In this paper, a hybrid multi-step-ahead forecasting model based on variational mode decomposition (VMD), fast multi-output relevance vector regression (FMRVR), and the multi-objective whale optimization algorithm (MOWOA) is proposed. VMD is employed to extract the primary mode for the carbon price. Then, FMRVR, which is used as the forecasting module, is built on the preprocessed data. To achieve high accuracy and stability, the MOWOA is utilized to optimize the kernel parameter and input the lag of the FMRVR. The proposed hybrid forecasting model is applied to carbon price series from three major regional carbon emission exchanges in China. Results show that the proposed VMD-FMRVR-MOWOA model achieves better performance compared to several other multi-output models in terms of forecasting accuracy and stability. The proposed model can be a potential and effective technique for multi-step-ahead carbon price forecasting in China’s three major regional emission exchanges.

scholarly journals An Integrated Framework of GRU Based on Improved Whale Optimization Algorithm for Flood Prediction

2021 ◽  
Author(s):  
Chunlei Ji ◽  
Tian Peng ◽  
Chu Zhang ◽  
Lei Hua ◽  
Wei Sun
Keyword(s):  
Time Series ◽  
Optimization Algorithm ◽  
Flood Control ◽  
Control Strategies ◽  
Whale Optimization Algorithm ◽  
Flood Prediction ◽  
Hybrid Strategy ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Using Data

Abstract Accurate prediction of floods is the first step in formulating flood control strategies and reducing flood disasters. This research proposes a deep learning model based on Gate Recurrent Unit (GRU), Random Forest Algorithm (RF), Whale Optimization Algorithm (WOA) and Optimal Variational Mode Decomposition (OVMD) for flood prediction. First, the random historical time series is decomposed using OVMD. Secondly, combined with the RF feature importance measurement, select features with high importance to obtain the optimal input set. Third, use the GRU model to predict all sub-models, and use the WOA algorithm to optimize the hyperparameters in the GRU model. This study also proposes a hybrid strategy to improve the traditional WOA algorithm and enhance the optimization ability of the WOA algorithm. Finally, the prediction results of all sub-modes were aggregated to generate the final prediction result. The model was validated using data from three hydrological stations in the upper, middle and lower reaches of the Minjiang river basin in China. Through the results of the experiment, it can be seen that the proposed prediction model can effectively predict the flood time series, and has better accuracy than other models.

scholarly journals A minimum cross-entropy multi-thresholds segmentation algorithm based on improved WOA

2021 ◽  
Vol 336 ◽  
pp. 07003
Author(s):  
Zhenkun Zhu ◽  
Yuan Sun
Keyword(s):  
Optimization Algorithm ◽  
Fitness Function ◽  
Whale Optimization Algorithm ◽  
Cross Entropy ◽  
Optimal Threshold ◽  
Invasive Weed ◽  
High Quality Image ◽  
Whale Optimization ◽  
Quality Image ◽  
Minimum Cross Entropy

Minimum cross-entropy is widely used in image segmentation for its effectiveness. However, when the algorithm is applied to multi-threshold segmentation, there are some problems such as large amount of calculation, time-consuming and poor practicability due to exhaustive search for the optimal threshold. Therefore, in this paper, a hybrid whale optimization algorithm (IWOA) which incorporates whale optimization algorithm (WOA) and invasive weed optimization (IWO) is proposed and the minimum cross-entropy is used as the fitness function of optimization algorithm to select the optimal threshold. It is established that IWOA algorithm is able to select the optimal threshold in more accuracy and segment high quality image than other algorithms.

scholarly journals Optimized ELM based on Whale Optimization Algorithm for gearbox diagnosis

2019 ◽  
Vol 255 ◽  
pp. 02003
Author(s):  
M. Firdaus Isham ◽  
M. Salman Leong ◽  
M. H. Lim ◽  
Z. A.B. Ahmad
Keyword(s):  
Optimization Algorithm ◽  
Learning Algorithm ◽  
Whale Optimization Algorithm ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Gearbox Vibration ◽  
Learning Machine ◽  
Hidden Layer ◽  
New Diagnosis

Extreme learning machine (ELM) is a fast and quick learning algorithm with better generalization performance. However, the randomness of input weight and hidden layer bias may affect the overall performance of ELM. This paper proposed a new approach to determine the optimized values of input weight and hidden layer bias for ELM using whale optimization algorithm (WOA), which we call WOA-ELM. An online gearbox vibration signals is used in this study. Empirical mode decomposition (EMD) and complementary mode decomposition (CEEMD) are used to decompose the signals into sub-signals known as intrinsic mode functions (IMFs). Then, statistical features are extracted from selected IMFs. WOA-ELM is used for classification of healthy and faulty condition of gearbox. The result shows that WOA-ELM provide better classification result as compared with conventional ELM. Therefore, this study provide a new diagnosis approach for gearbox application.

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