Shumin Sun¹, Peng Yu¹, Jiawei Xing¹, Yan Cheng¹, Song Yang¹, Qian Ai^2,*

Energy Engineering, Vol.120, No.12, pp. 2761-2782, 2023, DOI:10.32604/ee.2023.042635

Abstract Wind power prediction is very important for the economic dispatching of power systems containing wind power. In this work, a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and (long short-term memory) LSTM neural network is proposed and studied. First, the original data is prepossessed including removing outliers and filling in the gaps. Then, the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model. In addition, this study conducts seasonal classification of the annual data where… More >

Jin Wang¹, Zhen Liu^1,*, Ying Wang¹, Caifeng Wen^2,3, Jianwen Wang^2,3

Energy Engineering, Vol.120, No.5, pp. 1149-1162, 2023, DOI:10.32604/ee.2023.025209

Abstract Analyzing the strain signal of wind turbine blade is the key to studying the load of wind turbine blade, so as to ensure the safe and stable operation of wind turbine in natural environment. The strain signal of the wind turbine blade under continuous crosswind state has typical non-stationary and unsteady characteristics. The strain signal contains a lot of noise, which makes the analysis error. Therefore, it is very important to denoise and extract features of measured signals before signal analysis. In this paper, the joint algorithm of ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is used for… More >

Ahmad A. Mohammad^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1139-1156, 2023, DOI:10.32604/cmc.2023.035964

Abstract Exploiting modification direction (EMD) based data hiding techniques (DHTs) provide moderate data hiding capacity and high-quality stego images. The overflow problem and the cyclic nature of the extraction function essentially hinder their application in several fields in which reversibility is necessary. Thus far, the few EMD reversible DHTs are complex and numerically demanding. This paper presents a novel EMD-based reversible DHT using dual-image. Two novel 2 × 4 modification lookup tables are introduced, replacing the reference matrix used in similar techniques and eliminating the numerically demanding search step in similar techniques. In the embedding step, one of the modification tables modifies a… More >

Xiaoping Xie^*, Shijie Cheng, Xuyang Li

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2425-2448, 2023, DOI:10.32604/cmes.2023.023116

Abstract This paper proposed method that combined transmission path analysis (TPA) and empirical mode decomposition (EMD) envelope analysis to solve the vibration problem of an industrial robot. Firstly, the deconvolution filter time-domain TPA method is proposed to trace the source along with the time variation. Secondly, the TPA method positioned the main source of robotic vibration under typically different working conditions. Thirdly, independent vibration testing of the Rotate Vector (RV) reducer is conducted under different loads and speeds, which are key components of an industrial robot. The method of EMD and Hilbert envelope was used to extract the fault feature of… More >

Anchal Tyagi¹, Salem Alelyani², Sapna Katiyar³, Mohammad Rashid Hussain^2,*, Rijwan Khan³, Mohammed Saleh Alsaqer²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1423-1438, 2023, DOI:10.32604/csse.2023.026057

Abstract An image can be degraded due to many environmental factors like foggy or hazy weather, low light conditions, extra light conditions etc. Image captured under the poor light conditions is generally known as non-uniform illumination image. Non-uniform illumination hides some important information present in an image during the image capture Also, it degrades the visual quality of image which generates the need for enhancement of such images. Various techniques have been present in literature for the enhancement of such type of images. In this paper, a novel architecture has been proposed for enhancement of poor illumination images which uses radial… More >

Hao Han, Wei Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1353-1370, 2023, DOI:10.32604/cmes.2022.021494

Abstract Accurate prediction of ship motion is very important for ensuring marine safety, weapon control, and aircraft carrier landing, etc. Ship motion is a complex time-varying nonlinear process which is affected by many factors. Time series analysis method and many machine learning methods such as neural networks, support vector machines regression (SVR) have been widely used in ship motion predictions. However, these single models have certain limitations, so this paper adopts a multi-model prediction method. First, ensemble empirical mode decomposition (EEMD) is used to remove noise in ship motion data. Then the random forest (RF) prediction model optimized by genetic algorithm… More >

Wajaree Weera¹, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Sakda Noinang⁴, Thongchai Botmart^1,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4833-4849, 2022, DOI:10.32604/cmc.2022.030888

Abstract The purpose of this research is to construct an integrated neuro swarming scheme using the procedures of the artificial neural networks (ANNs) with the use of global search particle swarm optimization (PSO) along with the competent local search interior-point programming (IPP) called as ANN-PSOIPP. The proposed computational scheme is implemented for the numerical simulations of the third order nonlinear delay differential Emden-Fowler model (TON-DD-EFM). The TON-DD-EFM is based on two types along with the particulars of shape factor, delayed terms, and singular points. A merit function is performed using the optimization of PSOIPP to find the solutions to the TON-DD-EFM.… More >

Debiao Meng^1,2,3,*, Hongtao Wang¹, Shiyuan Yang¹, Zhiyuan Lv¹, Zhengguo Hu¹, Zihao Wang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 543-558, 2022, DOI:10.32604/cmes.2022.018123

Abstract In a wind turbine, the rolling bearing is the critical component. However, it has a high failure rate. Therefore, the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment. In this study, the failure form and the corresponding reason for the failure are discussed firstly. Then, the natural frequency and the characteristic frequency are analyzed. The Empirical Mode Decomposition (EMD) algorithm is used to extract the characteristics of the vibration signal of the rolling bearing. Moreover, the eigenmode function is obtained and then filtered by… More >

Khomdet Phapatanaburi¹, Kasidit kokkhunthod², Longbiao Wang³, Talit Jumphoo², Monthippa Uthansakul², Anyaporn Boonmahitthisud⁴, Peerapong Uthansakul^2,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3029-3044, 2021, DOI:10.32604/cmc.2021.014433

Abstract A brainwave classification, which does not involve any limb movement and stimulus for character-writing applications, benefits impaired people, in terms of practical communication, because it allows users to command a device/computer directly via electroencephalogram signals. In this paper, we propose a new framework based on Empirical Mode Decomposition (EMD) features along with the Gaussian Mixture Model (GMM) and Kernel Extreme Learning Machine (KELM)-based classifiers. For this purpose, firstly, we introduce EMD to decompose EEG signals into Intrinsic Mode Functions (IMFs), which actually are used as the input features of the brainwave classification for the character-writing application. We hypothesize that EMD… More >

Mengcheng Sun^1,2, Weiya Xu^1,2,*, Huanling Wang^1,3, Qingxiang Meng^1,2, Long Yan^1,2, Wei-Chau Xie⁴

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 1057-1074, 2021, DOI:10.32604/cmc.2020.012537

Abstract The narrowing deformation of reservoir valley during the initial operation period threatens the long-term safety of the dam, and an accurate prediction of valley deformation (VD) remains a challenging part of risk mitigation. In order to enhance the accuracy of VD prediction, a novel hybrid model combining Ensemble empirical mode decomposition based interval threshold denoising (EEMD-ITD), Differential evolutions—Shuffled frog leaping algorithm (DE-SFLA) and Least squares support vector machine (LSSVM) is proposed. The non-stationary VD series is firstly decomposed into several stationary subseries by EEMD; then, ITD is applied for redundant information denoising on special sub-series, and the denoised deformation is… More >