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  • Open Access

    ARTICLE

    Modeling the Dynamics of Tuberculosis with Vaccination, Treatment, and Environmental Impact: Fractional Order Modeling

    Muhammad Altaf Khan1,*, Mahmoud H. DarAssi2, Irfan Ahmad3, Noha Mohammad Seyam4, Ebraheem Alzahrani5

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1365-1394, 2024, DOI:10.32604/cmes.2024.053681 - 27 September 2024

    Abstract A mathematical model is designed to investigate Tuberculosis (TB) disease under the vaccination, treatment, and environmental impact with real cases. First, we introduce the model formulation in non-integer order derivative and then, extend the model into fractional order derivative. The fractional system’s existence, uniqueness, and other relevant properties are shown. Then, we study the stability analysis of the equilibrium points. The disease-free equilibrium (DFE) is locally asymptotically stable (LAS) when . Further, we show the global asymptotical stability (GAS) of the endemic equilibrium (EE) for and for . The existence of bifurcation analysis in the More >

  • Open Access

    ARTICLE

    Design of a Multifrequency Signal Parameter Estimation Method for the Distribution Network Based on HIpST

    Bin Liu1, Shuai Liang1, Renjie Ding1, Shuguang Li2,*

    Energy Engineering, Vol.121, No.3, pp. 729-746, 2024, DOI:10.32604/ee.2023.044224 - 27 February 2024

    Abstract The application of traditional synchronous measurement methods is limited by frequent fluctuations of electrical signals and complex frequency components in distribution networks. Therefore, it is critical to find solutions to the issues of multifrequency parameter estimation and synchronous measurement estimation accuracy in the complex environment of distribution networks. By utilizing the multifrequency sensing capabilities of discrete Fourier transform signals and Taylor series for dynamic signal processing, a multifrequency signal estimation approach based on HT-IpDFT-STWLS (HIpST) for distribution networks is provided. First, by introducing the Hilbert transform (HT), the influence of noise on the estimation algorithm… More >

  • Open Access

    ARTICLE

    A Time-Varying Parameter Estimation Method for Physiological Models Based on Physical Information Neural Networks

    Jiepeng Yao1,2, Zhanjia Peng1,2, Jingjing Liu1,2, Chengxiao Fan1,2, Zhongyi Wang1,2,3, Lan Huang1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2243-2265, 2023, DOI:10.32604/cmes.2023.028101 - 03 August 2023

    Abstract In the establishment of differential equations, the determination of time-varying parameters is a difficult problem, especially for equations related to life activities. Thus, we propose a new framework named BioE-PINN based on a physical information neural network that successfully obtains the time-varying parameters of differential equations. In the proposed framework, the learnable factors and scale parameters are used to implement adaptive activation functions, and hard constraints and loss function weights are skillfully added to the neural network output to speed up the training convergence and improve the accuracy of physical information neural networks. In this… More >

  • Open Access

    ARTICLE

    On a New Version of Weibull Model: Statistical Properties, Parameter Estimation and Applications

    Hassan Okasha1,2, Mazen Nassar1,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2219-2241, 2023, DOI:10.32604/cmes.2023.028783 - 03 August 2023

    Abstract In this paper, we introduce a new four-parameter version of the traditional Weibull distribution. It is able to provide seven shapes of hazard rate, including constant, decreasing, increasing, unimodal, bathtub, unimodal then bathtub, and bathtub then unimodal shapes. Some basic characteristics of the proposed model are studied, including moments, entropies, mean deviations and order statistics, and its parameters are estimated using the maximum likelihood approach. Based on the asymptotic properties of the estimators, the approximate confidence intervals are also taken into consideration in addition to the point estimators. We examine the effectiveness of the maximum More >

  • Open Access

    ARTICLE

    State Estimation Moving Window Gradient Iterative Algorithm for Bilinear Systems Using the Continuous Mixed p-norm Technique

    Wentao Liu, Junxia Ma, Weili Xiong*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 873-892, 2023, DOI:10.32604/cmes.2022.020565 - 31 August 2022

    Abstract This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space models in the presence of disturbances. A bilinear state observer is designed for deriving identification algorithms to estimate the state variables using the input-output data. Based on the bilinear state observer, a novel gradient iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous mixed p-norm cost function. The gain at each iterative step adapts to the data quality so that the algorithm has good robustness to the noise disturbance. Furthermore, to More >

  • Open Access

    ARTICLE

    Parameter Estimation Based on Censored Data under Partially Accelerated Life Testing for Hybrid Systems due to Unknown Failure Causes

    Mustafa Kamal*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1239-1269, 2022, DOI:10.32604/cmes.2022.017532 - 30 December 2021

    Abstract In general, simple subsystems like series or parallel are integrated to produce a complex hybrid system. The reliability of a system is determined by the reliability of its constituent components. It is often extremely difficult or impossible to get specific information about the component that caused the system to fail. Unknown failure causes are instances in which the actual cause of system failure is unknown. On the other side, thanks to current advanced technology based on computers, automation, and simulation, products have become incredibly dependable and trustworthy, and as a result, obtaining failure data for… More >

  • Open Access

    ARTICLE

    Optimal Parameter Estimation of Transmission Line Using Chaotic Initialized Time-Varying PSO Algorithm

    Abdullah Shoukat1, Muhammad Ali Mughal1,*, Saifullah Younus Gondal1, Farhana Umer2, Tahir Ejaz3, Ashiq Hussain1

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 269-285, 2022, DOI:10.32604/cmc.2022.021575 - 03 November 2021

    Abstract Transmission line is a vital part of the power system that connects two major points, the generation, and the distribution. For an efficient design, stable control, and steady operation of the power system, adequate knowledge of the transmission line parameters resistance, inductance, capacitance, and conductance is of great importance. These parameters are essential for transmission network expansion planning in which a new parallel line is needed to be installed due to increased load demand or the overhead line is replaced with an underground cable. This paper presents a method to optimally estimate the parameters using… More >

  • Open Access

    ARTICLE

    Optimal Tuning of FOPID-Like Fuzzy Controller for High-Performance Fractional-Order Systems

    Ahmed M. Nassef1,2,*, Hegazy Rezk1,3

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 171-180, 2022, DOI:10.32604/cmc.2022.019347 - 07 September 2021

    Abstract This paper addresses improvements in fractional order (FO) system performance. Although the classical proportional–integral–derivative (PID)-like fuzzy controller can provide adequate results for both transient and steady-state responses in both linear and nonlinear systems, the FOPID fuzzy controller has been proven to provide better results. This high performance was obtained thanks to the combinative benefits of FO and fuzzy-logic techniques. This paper describes how the optimal gains and FO parameters of the FOPID controller were obtained by the use of a modern optimizer, social spider optimization, in order to improve the response of fractional dynamical systems.… More >

  • Open Access

    ARTICLE

    Optimal Parameter Estimation of Proton Exchange Membrane Fuel Cells

    A. M. Abdullah1, Hegazy Rezk2,3,*, A. Hadad1, Mohamed K. Hassan1,4, A. F. Mohamed1,5

    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 619-631, 2021, DOI:10.32604/iasc.2021.018289 - 16 June 2021

    Abstract The problem of parameter estimation of the proton exchange membrane fuel cell (PEMFC) model plays a significant role in the simulation and optimization of a PEMFC system. In the current research, a moth flame optimization algorithm (MFOA) is used to identify the best parameters of PEMFC. Two different PEMFCs, Nedstack PS6, 6 kW, and SR-12 PEM 500 W are used to demonstrate the accuracy of the MFOA. Throughout the optimization process, seven unidentified parameters (1, 2, 3, 4, λ, ℛ, and B) of PEMFC are appointed to be decision variables. The fitness function, which needed to be… More >

  • Open Access

    ARTICLE

    Parameter Estimation of Alpha Power Inverted Topp-Leone Distribution with Applications

    Gamal M. Ibrahim1, Amal S. Hassan2, Ehab M. Almetwally3,*, Hisham M. Almongy4

    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 353-371, 2021, DOI:10.32604/iasc.2021.017586 - 16 June 2021

    Abstract We introduce a new two-parameter lifetime model, referred to alpha power transformed inverted Topp-Leone, derived by combining the alpha power transformation-G family with the inverted Topp-Leone distribution. Structural properties of the proposed distribution are implemented like; quantile function, residual and reversed residual life, Rényi entropy measure, moments and incomplete moments. The maximum likelihood, weighted least squares, maximum product of spacing, and Bayesian methods of estimation are considered. A simulation study is worked out to evaluate the restricted sample properties of the proposed distribution. Numerical results showed that the Bayesian estimates give more accurate results than… More >

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