scholarly journals Optimal Threshold Estimation using Grey Wolf Optimization for EMD-DTCWT Based ECG Denoising

2020 ◽  
Vol 8 (6) ◽  
pp. 2589-2596
Keyword(s):  
Adaptive Filtering ◽  
Data Driven ◽  
Optimal Threshold ◽  
Signal Denoising ◽  
Grey Wolf ◽  
Threshold Estimation ◽  
Complex Wavelet Transform ◽  
Grey Wolf Optimization ◽  
Mode Decomposition ◽  
Ecg Denoising

The noise reduction in the ECG has been focused for research in recent years, since desired reduction allows a better signal pre-processing, and allows to extract from it the maximum amount of efficient and meaningful information. This paper proposes an adaptive threshold technique using Empirical Mode Decomposition (EMD) and Dual-Tree Complex Wavelet Transform (DTCWT) for ECG signal denoising. Initially the data driven EMD technique is applied to get the IMFS and these IMFS further passed though DTCWT for filtration. To accomplish the better adaptive filtering process the optimal threshold is further calculated based on Grey Wolf Optimization (GWO). The performance evaluation is achieved on MIT-BIH database.

scholarly journals Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4890
Author(s):  
Mengran Zhou ◽  
Tianyu Hu ◽  
Kai Bian ◽  
Wenhao Lai ◽  
Feng Hu ◽  
...  
Keyword(s):  
Load Forecasting ◽  
Support Vector ◽  
Electric Load ◽  
Variational Mode Decomposition ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Short Term ◽  
Mode Decomposition ◽  
The Impact ◽  
Electric Load Forecasting

Short-term electric load forecasting plays a significant role in the safe and stable operation of the LO system and power market transactions. In recent years, with the development of new energy sources, more and more sources have been integrated into the grid. This has posed a serious challenge to short-term electric load forecasting. Focusing on load series with non-linear and time-varying characteristics, an approach to short-term electric load forecasting using a “decomposition and ensemble” framework is proposed in this paper. The method is verified using hourly load data from Oslo and the surrounding areas of Norway. First, the load series is decomposed into five components by variational mode decomposition (VMD). Second, a support vector regression (SVR) forecasting model is established for the five components to predict the electric load components, and the grey wolf optimization (GWO) algorithm is used to optimize the cost and gamma parameters of SVR. Finally, the predicted values of the five components are superimposed to obtain the final electric load forecasting results. In this paper, the proposed method is compared with GWO-SVR without modal decomposition and using empirical mode decomposition (EMD) to test the impact of VMD on prediction. This paper also compares the proposed method with the SVR model using VMD and other optimization algorithms. The four evaluation indexes of the proposed method are optimal: MAE is 71.65 MW, MAPE is 1.41%, MSE is 10,461.32, and R2 is 0.9834. This indicates that the proposed method has a good application prospect for short-term electric load forecasting.

scholarly journals Shoreline: Data-Driven Threshold Estimation of Online Reserves of Cryptocurrency Trading Platforms

2020 ◽  
Vol 34 (01) ◽  
pp. 1194-1201
Author(s):  
Xitong Zhang ◽  
He Zhu ◽  
Jiayu Zhou
Keyword(s):  
Empirical Studies ◽  
Gain Control ◽  
Data Driven ◽  
Optimal Threshold ◽  
Threshold Estimation ◽  
Trading Systems ◽  
Different Types ◽  
Dynamic Trading ◽  
Trading Networks ◽  
Digital Assets

With the proliferation of blockchain projects and applications, cryptocurrency exchanges, which provides exchange services among different types of cryptocurrencies, become pivotal platforms that allow customers to trade digital assets on different blockchains. Because of the anonymity and trustlessness nature of cryptocurrency, one major challenge of crypto-exchanges is asset safety, and all-time amount hacked from crypto-exchanges until 2018 is over $1.5 billion even with carefully maintained secure trading systems. The most critical vulnerability of crypto-exchanges is from the so-called hot wallet, which is used to store a certain portion of the total asset of an exchange and programmatically sign transactions when a withdraw happens. Whenever hackers managed to gain control over the computing infrastructure of the exchange, they usually immediately obtain all the assets in the hot wallet. It is important to develop network security mechanisms. However, the fact is that there is no guarantee that the system can defend all attacks. Thus, accurately controlling the available assets in the hot wallets becomes the key to minimize the risk of running an exchange. However, determining such optimal threshold remains a challenging task because of the complicated dynamics inside exchanges. In this paper, we propose Shoreline, a deep learning-based threshold estimation framework that estimates the optimal threshold of hot wallets from historical wallet activities and dynamic trading networks. We conduct extensive empirical studies on the real trading data from a trading platform and demonstrate the effectiveness of the proposed approach.

scholarly journals Salt and Pepper Noise Removal for Image Using Adaptive Pulse-Coupled Neural Network Optimized by Grey Wolf Optimization and Bidimensional Empirical Mode Decomposition

2018 ◽  
Vol 8 (10) ◽  
pp. 1977
Author(s):  
Min Cui ◽  
Yicheng Wu ◽  
Chenguang Wang ◽  
Xiaochen Liu ◽  
Chong Shen
Keyword(s):  
Neural Network ◽  
Noise Reduction ◽  
Image Denoising ◽  
Empirical Mode Decomposition ◽  
Parameter Determination ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Pulse Coupled Neural Network ◽  
Mode Decomposition ◽  
Bidimensional Empirical Mode Decomposition

Aimed at the problem of poor noise reduction effect and parameter uncertainty of pulse-coupled neural network (PCNN), a hybrid image denoising method, using an adaptive PCNN that has been optimized by grey wolf optimization (GWO) and bidimensional empirical mode decomposition (BEMD), is presented. The BEMD is used to decompose the original image into multilayer image components. After a GWO is run to complete PCNN parameter optimization, an adaptive PCNN filter method is used to remediate the polluted noise points that correspond to the different image components, from which a reconstruction of the denoised image components can then be obtained. From an analysis of the image denoising results, the main advantages of the proposed method are as follows: (i) the method effectively solves the deficiencies that arise from the critical PCNN parameter determination issue; (ii) the method effectively overcomes the problem of high-intensity noise effects by providing a more targeted and efficient noise reduction process; (iii) when using this method, the noise points are isolated, and the original pixel points are restored well, which can lead to preservation of image detail information. When compared with traditional image denoising process algorithms, the proposed method can yield a better noise suppression effect, based on indicators including analysis of mutual information (MI), structural similarity (SSIM), the peak signal-to-noise ratio (PSNR) and the standard deviation (STD). The feasibility and applicability of the proposed denoising algorithm are also demonstrated experimentally.

scholarly journals An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Dengyong Zhang ◽  
Shanshan Wang ◽  
Feng Li ◽  
Shang Tian ◽  
Jin Wang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Threshold Function ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Ecg Denoising

The electrocardiogram (ECG) signal can easily be affected by various types of noises while being recorded, which decreases the accuracy of subsequent diagnosis. Therefore, the efficient denoising of ECG signals has become an important research topic. In the paper, we proposed an efficient ECG denoising approach based on empirical mode decomposition (EMD), sample entropy, and improved threshold function. This method can better remove the noise of ECG signals and provide better diagnosis service for the computer-based automatic medical system. The proposed work includes three stages of analysis: (1) EMD is used to decompose the signal into finite intrinsic mode functions (IMFs), and according to the sample entropy of each order of IMF following EMD, the order of IMFs denoised is determined; (2) the new threshold function is adopted to denoise these IMFs after the order of IMFs denoised is determined; and (3) the signal is reconstructed and smoothed. The proposed method solves the shortcoming of discarding the first-order IMF directly in traditional EMD denoising and proposes a new threshold denoising function to improve the traditional soft and hard threshold functions. We further conduct simulation experiments of ECG signals from the MIT-BIH database, in which three types of noise are simulated: white Gaussian noise, electromyogram (EMG), and power line interference. The experimental results show that the proposed method is robust to a variety of noise types. Moreover, we analyze the effectiveness of the proposed method under different input SNR with reference to improving SNR ( SNR imp ) and mean square error ( MSE ), then compare the denoising algorithm proposed in this paper with previous ECG signal denoising techniques. The results demonstrate that the proposed method has a higher SNR imp and a lower MSE . Qualitative and quantitative studies demonstrate that the proposed algorithm is a good ECG signal denoising method.

scholarly journals A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3459
Author(s):  
Azim Heydari ◽  
Meysam Majidi Nezhad ◽  
Mehdi Neshat ◽  
Davide Astiaso Garcia ◽  
Farshid Keynia ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Empirical Mode Decomposition ◽  
Energy Production ◽  
Forecasting Model ◽  
Group Method ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Mode Decomposition ◽  
Combined Forecasting

A cost-effective and efficient wind energy production trend leads to larger wind turbine generators and drive for more advanced forecast models to increase their accuracy. This paper proposes a combined forecasting model that consists of empirical mode decomposition, fuzzy group method of data handling neural network, and grey wolf optimization algorithm. A combined K-means and identifying density-based local outliers is applied to detect and clean the outliers of the raw supervisory control and data acquisition data in the proposed forecasting model. Moreover, the empirical mode decomposition is employed to decompose signals and pre-processing data. The fuzzy GMDH neural network is a forecaster engine to estimate the future amount of wind turbines energy production, where the grey wolf optimization is used to optimize the fuzzy GMDH neural network parameters in order to achieve a lower forecasting error. Moreover, the model has been applied using actual data from a pilot onshore wind farm in Sweden. The obtained results indicate that the proposed model has a higher accuracy than others in the literature and provides single and combined forecasting models in different time-steps ahead and seasons.

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

Sign in / Sign up

Export Citation Format

Share Document