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Search Results (11)
  • Open Access

    ARTICLE

    Advancing Brain Tumor Classification: Evaluating the Efficacy of Machine Learning Models Using Magnetic Resonance Imaging

    Khalid Jamil1, Wahab Khan1, Bilal Khan2, Sarwar Shah Khan2,*

    Digital Engineering and Digital Twin, Vol.3, pp. 1-16, 2025, DOI:10.32604/dedt.2025.058943 - 28 February 2025

    Abstract Brain tumors are one of the deadliest cancers, partly because they’re often difficult to detect early or with precision. Standard Magnetic Resonance Imaging (MRI) imaging, though essential, has limitations, it can miss subtle or early-stage tumors, which delays diagnosis and affects patient outcomes. This study aims to tackle these challenges by exploring how machine learning (ML) can improve the accuracy of brain tumor identification from MRI scans. Motivated by the potential for artificial intillegence (AI) to boost diagnostic accuracy where traditional methods fall short, we tested several ML models, with a focus on the K-Nearest More >

  • Open Access

    ARTICLE

    Orthogonal Probability Approximation for Highly Accurate and Efficient Orbit Uncertainty Propagation

    Pugazhenthi Sivasankar1,*, Austin B. Probe2, Tarek A. Elgohary1

    Digital Engineering and Digital Twin, Vol.2, pp. 169-205, 2024, DOI:10.32604/dedt.2024.052805 - 31 December 2024

    Abstract In Space Situational Awareness (SSA), accurate and efficient uncertainty quantification and propagation are essential for various applications, such as conjunction analysis, track correlation, and orbit prediction. The propagation of the probability density function (PDF) in nonlinear systems results in non-Gaussian distributions, which are difficult to approximate. Furthermore, the computational cost of approximating the PDF increases exponentially with the number of random variables, a phenomenon known as the curse of dimensionality. To address these challenges, the Orthogonal Probability Approximation (OPA) method is presented for high-fidelity uncertainty propagation and PDF approximation in nonlinear dynamical systems. The method… More >

  • Open Access

    ARTICLE

    Research on Substation Siting Based on a 3D GIS Platform and an Improved BP Neural Network

    Yao Jin1,2,*, Jie Zhao1,2, Xiaozhe Tan1,2, Linghou Miao1,2, Wenxing Yu1,2

    Digital Engineering and Digital Twin, Vol.2, pp. 131-144, 2024, DOI:10.32604/dedt.2024.048142 - 31 December 2024

    Abstract Substation siting is an important foundation and a key task in power system planning. The article is based on a three-dimensional GIS platform combined with an improved BP neural network algorithm and proposes a substation siting method that is more efficient, accurate and provides a better user experience. Firstly, the BP algorithm is enhanced to improve its convergence speed and computational efficiency for a more accurate and reasonable calculation of optimal site selection. Then, a 24-item selection index system with 7 categories is proposed, which provides quantifiable data support and an evaluation basis for substation… More >

  • Open Access

    ARTICLE

    Approach for the Simulation of Linear PDEs with Constant Coefficients, Testing Multi-Dimensional Helmholtz and Wave Equations

    Chein-Shan Liu, Chung-Lun Kuo*

    Digital Engineering and Digital Twin, Vol.2, pp. 145-167, 2024, DOI:10.32604/dedt.2024.042804 - 31 December 2024

    Abstract A new concept of projective solution is introduced for the second-order linear partial differential equations (PDEs) endowed with constant coefficients. In terms of a projective variable the PDE is transformed to a second-order ordinary differential equation (ODE) with constant coefficients at the first time. The characteristic form appears as the coefficient preceding the second-order derivative term. Depending on the characteristic form and coefficients we can derive various parameters-dependent particular solutions, which can be adopted as the bases to expand the solution. The Helmholtz and wave equations are solved by the projection method. We project the… More >

  • Open Access

    ARTICLE

    Supervised Learning for Finite Element Analysis of Holes under Biaxial Load

    Wai Tuck Chow*, Jia Tai Lau

    Digital Engineering and Digital Twin, Vol.2, pp. 103-130, 2024, DOI:10.32604/dedt.2024.044545 - 06 May 2024

    Abstract This paper presents a novel approach to using supervised learning with a shallow neural network to increase the efficiency of the finite element analysis of holes under biaxial load. With this approach, the number of elements in the finite element analysis can be reduced while maintaining good accuracy. The neural network will be used to predict the maximum stress for holes of different configurations such as holes in a finite-width plate (2D), multiple holes (2D), staggered holes (2D), and holes in an infinite plate (3D). The predictions are based on their respective coarse mesh with… More >

  • Open Access

    REVIEW

    A Review on Finite Element Alternating Methods for Analyzing 2D and 3D Cracks

    Jai Hak Park*

    Digital Engineering and Digital Twin, Vol.2, pp. 79-101, 2024, DOI:10.32604/dedt.2024.047280 - 25 March 2024

    Abstract A finite element alternating method has been known as a very convenient and accurate method to solve two and three-dimensional crack problems. In this method, a general crack problem is solved by a superposition of two solutions. One is a finite element solution for a finite body without a crack, and the other is an analytical solution for a crack in an infinite body. Since a crack is not considered in a finite element model, generating a model is very simple. The method is especially very convenient for a fatigue crack growth simulation. Over the More >

  • Open Access

    ARTICLE

    Physics Based Digital Twin Modelling from Theory to Concept Implementation Using Coiled Springs Used in Suspension Systems

    Mohamed Ammar1,*, Alireza Mousavi1, Hamed Al-Raweshidy2,*

    Digital Engineering and Digital Twin, Vol.2, pp. 1-31, 2024, DOI:10.32604/dedt.2023.044930 - 31 January 2024

    Abstract The advent of technology around the globe based on the Internet of Things, Cloud Computing, Big Data, Cyber-Physical Systems, and digitalisation. This advancement introduced industry 4.0. It is challenging to measure how enterprises adopt the new technologies. Industry 4.0 introduced Digital Twins, given that no specific terms or definitions are given to Digital Twins still challenging to define or conceptualise the Digital Twins. Many academics and industries still use old technologies, naming it Digital Twins. This young technology is in danger of reaching the plateau despite the immense benefit to sectors. This paper proposes a… More >

  • Open Access

    ARTICLE

    Large-Scale 3D Thermal Transfer Analysis with 1D Model of Piped Cooling Water

    Shigeki Kaneko1, Naoto Mitsume2, Shinobu Yoshimura1,*

    Digital Engineering and Digital Twin, Vol.2, pp. 33-48, 2024, DOI:10.32604/dedt.2023.044279 - 31 January 2024

    Abstract In an integrated coal gasification combined cycle plant, cooling pipes are installed in the gasifier reactor and water cooling is executed to avoid reaching an excessively high temperature. To accelerate the design, it is necessary to develop an analysis system that can simulate the cooling operation within the practical computational time. In the present study, we assumed the temperature fields of the cooled object and the cooling water to be governed by the three-dimensional (3D) heat equation and the one-dimensional (1D) convection-diffusion equation, respectively. Although some existing studies have employed similar modeling, the applications have… More >

  • Open Access

    ARTICLE

    Stability and Error Analysis of Reduced-Order Methods Based on POD with Finite Element Solutions for Nonlocal Diffusion Problems

    Haolun Zhang1, Mengna Yang1, Jie Wei2, Yufeng Nie2,*

    Digital Engineering and Digital Twin, Vol.2, pp. 49-77, 2024, DOI:10.32604/dedt.2023.044180 - 31 January 2024

    Abstract This paper mainly considers the formulation and theoretical analysis of the reduced-order numerical method constructed by proper orthogonal decomposition (POD) for nonlocal diffusion problems with a finite range of nonlocal interactions. We first set up the classical finite element discretization for nonlocal diffusion equations and briefly explain the difference between nonlocal and partial differential equations (PDEs). Nonlocal models have to handle double integrals when using finite element methods (FEMs), which causes the generation of algebraic systems to be more challenging and time-consuming, and discrete systems have less sparsity than those for PDEs. So we establish… More >

  • Open Access

    ARTICLE

    An Adaptive Parallel Feedback-Accelerated Picard Iteration Method for Simulating Orbit Propagation

    Changtao Wang, Honghua Dai*, Wenchuan Yang

    Digital Engineering and Digital Twin, Vol.1, pp. 3-13, 2023, DOI:10.32604/dedt.2023.044210 - 28 December 2023

    Abstract A novel Adaptive Parallel Feedback-Accelerated Picard Iteration (AP-FAPI) method is proposed to meet the requirements of various aerospace missions for fast and accurate orbit propagation. The Parallel Feedback-Accelerated Picard Iteration (P-FAPI) method is an advanced iterative collocation method. With large-step computing and parallel acceleration, the P-FAPI method outperforms the traditional finite-difference-based methods, which require small-step and serial integration to ensure accuracy. Although efficient and accurate, the P-FAPI method suffers extensive trials in tuning method parameters, strongly influencing its performance. To overcome this problem, we propose the AP-FAPI method based on the relationship between the parameters More >

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