Shaoxiong Wu¹, Ruoxin Li¹, Xiaofeng Tao¹, Hailong Wu^1,*, Ping Miao¹, Yang Lu¹, Yanyan Lu¹, Qi Liu², Li Pan²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

Wangpeng He^1,*, Yue Zhou¹, Xiaoya Guo², Deshun Hu¹, Junjie Ye³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2495-2511, 2023, DOI:10.32604/cmes.2023.027896 - 03 August 2023

Abstract In today’s world, smart electric vehicles are deeply integrated with smart energy, smart transportation and smart cities. In electric vehicles (EVs), owing to the harsh working conditions, mechanical parts are prone to fatigue damages, which endanger the driving safety of EVs. The practice has proved that the identification of periodic impact characteristics (PICs) can effectively indicate mechanical faults. This paper proposes a novel model-based approach for intelligent fault diagnosis of mechanical transmission train in EVs. The essential idea of this approach lies in the fusion of statistical information and model information from a dynamic process.… More >

Qixin Lan, Binqiang Chen^*, Bin Yao

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 2017-2037, 2023, DOI:10.32604/cmes.2023.025307 - 06 February 2023

Abstract Many kinds of electrical equipment are used in civil and building engineering. The motor is one of the main power components of this electrical equipment, which can provide stable power output. During the long-term use of motors, various motor faults may occur, which affects the normal use of electrical equipment and even causes accidents. It is significant to apply fault diagnosis for the motors at the construction site. Aiming at the problem that signal data of faulty motor lack diversity, this research designs a multi-layer perceptron Wasserstein generative adversarial network, which is used to enhance… More >

W. A. Shaikh^1,2,*, S. F. Shah², S. M. Pandhiani³, M. A. Solangi²

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1517-1532, 2022, DOI:10.32604/cmes.2022.017822 - 30 December 2021

Abstract This investigative study is focused on the impact of wavelet on traditional forecasting time-series models, which significantly shows the usage of wavelet algorithms. Wavelet Decomposition (WD) algorithm has been combined with various traditional forecasting time-series models, such as Least Square Support Vector Machine (LSSVM), Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS) and their effects are examined in terms of the statistical estimations. The WD has been used as a mathematical application in traditional forecast modelling to collect periodically measured parameters, which has yielded tremendous constructive outcomes. Further, it is observed that the More >

Hai Liu^1,*, Xiangchao Zhou^2,3

Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1493-1501, 2020, DOI:10.32604/iasc.2020.012159 - 24 December 2020

Abstract Aiming at the problems of poor brightness control effect and low registration accuracy in traditional multi focus image registration, a wavelet multi-scale multi focus image region fusion registration method is proposed. The multi-scale Retinex algorithm is used to enhance the image, the wavelet decomposition similarity analysis is used for image interpolation, and the EMD method is used to decompose the multi focus image. Finally, the image reconstruction is completed and the multi focus image registration is realized. In order to verify the multi focus image fusion registration effect of different methods, a comparative experiment was More >

T. M. Alamelu Manghai¹, R. Jegadeeshwaran²

Structural Durability & Health Monitoring, Vol.11, No.2, pp. 149-167, 2017, DOI:10.3970/sdhm.2017.011.149

Abstract Hydraulic brakes in automobiles are an important control component used not only for the safety of the passenger but also for others moving on the road. Therefore, monitoring the condition of the brake components is inevitable. The brake elements can be monitored by studying the vibration characteristics obtained from the brake system using a proper signal processing technique through machine learning approaches. The vibration signals were captured using an accelerometer sensor under a various fault condition. The acquired vibration signals were processed for extracting meaningful information as features. The condition of the brake system can More >

Nicolas Ali Libre^1,2, Arezoo Emdadi², Edward J. Kansa^3,4, Mohammad Shekarchi², Mohammad Rahimian²

CMES-Computer Modeling in Engineering & Sciences, Vol.50, No.2, pp. 161-190, 2009, DOI:10.3970/cmes.2009.050.161

Abstract We present a wavelet based adaptive scheme and investigate the efficiency of this scheme for solving nearly singular potential PDEs over irregularly shaped domains. For a problem defined over Ω∈ℜ^d, the boundary of an irregularly shaped domain, Γ, is defined as a boundary curve that is a product of a Heaviside function along the normal direction and a piecewise continuous tangential curve. The link between the original wavelet based adaptive method presented in Libre, Emdadi, Kansa, Shekarchi, and Rahimian (2008, 2009) or LEKSR method and the generalized one is given through the use of simple Heaviside More >

Nicolas Ali Libre^1,2, Arezoo Emdadi², Edward J. Kansa^3,4, Mohammad Shekarchi², Mohammad Rahimian²

CMES-Computer Modeling in Engineering & Sciences, Vol.38, No.3, pp. 263-284, 2008, DOI:10.3970/cmes.2008.038.263

Abstract We present a wavelet based adaptive scheme and investigate the efficiency of this scheme for solving nearly singular potential PDEs. Multiresolution wavelet analysis (MRWA) provides a firm mathematical foundation by projecting the solution of PDE onto a nested sequence of approximation spaces. The wavelet coefficients then were used as an estimation of the sensible regions for node adaptation. The proposed adaptation scheme requires negligible calculation time due to the existence of the fast Discrete Wavelet Transform (DWT). Certain aspects of the proposed adaptive scheme are discussed through numerical examples. It has been shown that the More >