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  • Open Access


    Extraction of Strain Characteristic Signals from Wind Turbine Blades Based on EEMD-WT

    Jin Wang1, Zhen Liu1,*, Ying Wang1, Caifeng Wen2,3, Jianwen Wang2,3

    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 >

  • Open Access


    An Efficient EMD-Based Reversible Data Hiding Technique Using Dual Stego Images

    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 >

  • Open Access


    Vibration Diagnosis and Optimization of Industrial Robot Based on TPA and EMD Methods

    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 >

  • Open Access


    Radial Basis Approximations Based BEMD for Enhancement of Non-Uniform Illumination Images

    Anchal Tyagi1, Salem Alelyani2, Sapna Katiyar3, Mohammad Rashid Hussain2,*, 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 >

  • Open Access


    A Hybrid BPNN-GARF-SVR Prediction Model Based on EEMD for Ship Motion

    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 >

  • Open Access


    Swarming Computational Efficiency to Solve a Novel Third-Order Delay Differential Emden-Fowler System

    Wajaree Weera1, Zulqurnain Sabir2, Muhammad Asif Zahoor Raja3, Sakda Noinang4, Thongchai Botmart1,*

    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 >

  • Open Access


    Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

    Debiao Meng1,2,3,*, 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 >

  • Open Access


    Brainwave Classification for Character-Writing Application Using EMD-Based GMM and KELM Approaches

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

    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 >

  • Open Access


    A Novel Hybrid Intelligent Prediction Model for Valley Deformation: A Case Study in Xiluodu Reservoir Region, China

    Mengcheng Sun1,2, Weiya Xu1,2,*, Huanling Wang1,3, Qingxiang Meng1,2, Long Yan1,2, 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 >

  • Open Access


    Wind Turbine Drivetrain Expert Fault Detection System: Multivariate Empirical Mode Decomposition based Multi-sensor Fusion with Bayesian Learning Classification

    R. Uma Maheswari1,*, R. Umamaheswari2

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 479-488, 2020, DOI:10.32604/iasc.2020.013924

    Abstract To enhance the predictive condition-based maintenance (CBMS), a reliable automatic Drivetrain fault detection technique based on vibration monitoring is proposed. Accelerometer sensors are mounted on a wind turbine drivetrain at different spatial locations to measure the vibration from multiple vibration sources. In this work, multi-channel signals are fused and monocomponent modes of oscillation are reconstructed by the Multivariate Empirical Mode Decomposition (MEMD) Technique. Noise assisted methodology is adapted to palliate the mixing of modes with common frequency scales. The instantaneous amplitude envelope and instantaneous frequency are estimated with the Hilbert transform. Low order and high order statistical moments, signal feature… More >

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