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  • Open Access

    ARTICLE

    Migratable Power System Transient Stability Assessment Method Based on Improved XGBoost

    Ying Qu1, Jinhao Wang1, Xueting Cheng1, Jie Hao1, Weiru Wang1, Zhewen Niu2, Yuxiang Wu2,*

    Energy Engineering, Vol.121, No.7, pp. 1847-1863, 2024, DOI:10.32604/ee.2024.048300

    Abstract The data-driven transient stability assessment (TSA) of power systems can predict online real-time prediction by learning the temporal features before and after faults. However, the accuracy of the assessment is limited by the quality of the data and has weak transferability. Based on this, this paper proposes a method for TSA of power systems based on an improved extreme gradient boosting (XGBoost) model. Firstly, the gradient detection method is employed to remove noise interference while maintaining the original time series trend. On this basis, a focal loss function is introduced to guide the training of… More >

  • Open Access

    ARTICLE

    On Multi-Granulation Rough Sets with Its Applications

    Radwan Abu-Gdairi1, R. Mareay2,*, M. Badr3

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1025-1038, 2024, DOI:10.32604/cmc.2024.048647

    Abstract Recently, much interest has been given to multi-granulation rough sets (MGRS), and various types of MGRS models have been developed from different viewpoints. In this paper, we introduce two techniques for the classification of MGRS. Firstly, we generate multi-topologies from multi-relations defined in the universe. Hence, a novel approximation space is established by leveraging the underlying topological structure. The characteristics of the newly proposed approximation space are discussed. We introduce an algorithm for the reduction of multi-relations. Secondly, a new approach for the classification of MGRS based on neighborhood concepts is introduced. Finally, a real-life More >

  • Open Access

    REVIEW

    Saddlepoint Approximation Method in Reliability Analysis: A Review

    Debiao Meng1,2,*, Yipeng Guo1,2, Yihe Xu3, Shiyuan Yang1,2,*, Yongqiang Guo4, Lidong Pan4, Xinkai Guo2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2329-2359, 2024, DOI:10.32604/cmes.2024.047507

    Abstract The escalating need for reliability analysis (RA) and reliability-based design optimization (RBDO) within engineering challenges has prompted the advancement of saddlepoint approximation methods (SAM) tailored for such problems. This article offers a detailed overview of the general SAM and summarizes the method characteristics first. Subsequently, recent enhancements in the SAM theoretical framework are assessed. Notably, the mean value first-order saddlepoint approximation (MVFOSA) bears resemblance to the conceptual framework of the mean value second-order saddlepoint approximation (MVSOSA); the latter serves as an auxiliary approach to the former. Their distinction is rooted in the varying expansion orders… More >

  • Open Access

    REVIEW

    An Overview of Sequential Approximation in Topology Optimization of Continuum Structure

    Kai Long1, Ayesha Saeed1, Jinhua Zhang2, Yara Diaeldin1, Feiyu Lu1, Tao Tao3, Yuhua Li1,*, Pengwen Sun4, Jinshun Yan5

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 43-67, 2024, DOI:10.32604/cmes.2023.031538

    Abstract This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures. These structures, commonly encountered in engineering applications, often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables. As a result, sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges. Over the past several decades, topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds. In comparison… More >

  • Open Access

    ARTICLE

    Bifurcation Analysis of a Nonlinear Vibro-Impact System with an Uncertain Parameter via OPA Method

    Dongmei Huang1, Dang Hong2, Wei Li1,*, Guidong Yang1, Vesna Rajic3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 509-524, 2024, DOI:10.32604/cmes.2023.029215

    Abstract In this paper, the bifurcation properties of the vibro-impact systems with an uncertain parameter under the impulse and harmonic excitations are investigated. Firstly, by means of the orthogonal polynomial approximation (OPA) method, the nonlinear damping and stiffness are expanded into the linear combination of the state variable. The condition for the appearance of the vibro-impact phenomenon is to be transformed based on the calculation of the mean value. Afterwards, the stochastic vibro-impact system can be turned into an equivalent high-dimensional deterministic non-smooth system. Two different Poincaré sections are chosen to analyze the bifurcation properties and… More >

  • Open Access

    ARTICLE

    Fusing Supervised and Unsupervised Measures for Attribute Reduction

    Tianshun Xing, Jianjun Chen*, Taihua Xu, Yan Fan

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 561-581, 2023, DOI:10.32604/iasc.2023.037874

    Abstract It is well-known that attribute reduction is a crucial action of rough set. The significant characteristic of attribute reduction is that it can reduce the dimensions of data with clear semantic explanations. Normally, the learning performance of attributes in derived reduct is much more crucial. Since related measures of rough set dominate the whole process of identifying qualified attributes and deriving reduct, those measures may have a direct impact on the performance of selected attributes in reduct. However, most previous researches about attribute reduction take measures related to either supervised perspective or unsupervised perspective, which… More >

  • Open Access

    ARTICLE

    Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

    Amarendra Reddy Panyala1,2, M. Baskar3,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3883-3899, 2023, DOI:10.32604/csse.2023.037050

    Abstract The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFA-CNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey… More >

  • Open Access

    ARTICLE

    Improved QoS-Secure Routing in MANET Using Real-Time Regional ME Feature Approximation

    Y. M. Mahaboob John1,*, G. Ravi2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3653-3666, 2023, DOI:10.32604/csse.2023.036916

    Abstract Mobile Ad-hoc Network (MANET) routing problems are thoroughly studied several approaches are identified in support of MANET. Improve the Quality of Service (QoS) performance of MANET is achieving higher performance. To reduce this drawback, this paper proposes a new secure routing algorithm based on real-time partial ME (Mobility, energy) approximation. The routing method RRME (Real-time Regional Mobility Energy) divides the whole network into several parts, and each node’s various characteristics like mobility and energy are randomly selected neighbors accordingly. It is done in the path discovery phase, estimated to identify and remove malicious nodes. In… More >

  • Open Access

    ARTICLE

    Approximations by Ideal Minimal Structure with Chemical Application

    Rodyna A. Hosny1, Radwan Abu-Gdairi2, Mostafa K. El-Bably3,*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3073-3085, 2023, DOI:10.32604/iasc.2023.034234

    Abstract The theory of rough set represents a non-statistical methodology for analyzing ambiguity and imprecise information. It can be characterized by two crisp sets, named the upper and lower approximations that are used to determine the boundary region and accurate measure of any subset. This article endeavors to achieve the best approximation and the highest accuracy degree by using the minimal structure approximation space via ideal . The novel approach (indicated by ) modifies the approximation space to diminish the boundary region and enhance the measure of accuracy. The suggested method is more accurate than Pawlak’s More >

  • Open Access

    ARTICLE

    A Numerical Investigation Based on Exponential Collocation Method for Nonlinear SITR Model of COVID-19

    Mohammad Aslefallah1, Şuayip Yüzbaşi2, Saeid Abbasbandy1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1687-1706, 2023, DOI:10.32604/cmes.2023.025647

    Abstract In this work, the exponential approximation is used for the numerical simulation of a nonlinear SITR model as a system of differential equations that shows the dynamics of the new coronavirus (COVID-19). The SITR mathematical model is divided into four classes using fractal parameters for COVID-19 dynamics, namely, susceptible (S), infected (I), treatment (T), and recovered (R). The main idea of the presented method is based on the matrix representations of the exponential functions and their derivatives using collocation points. To indicate the usefulness of this method, we employ it in some cases. For error More > Graphic Abstract

    A Numerical Investigation Based on Exponential Collocation Method for Nonlinear SITR Model of COVID-19

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