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  • Open Access

    ARTICLE

    Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

    Jing Gao*, Mingxuan Ji, Hongjiang Wang, Zhongxiao Du

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5017-5030, 2024, DOI:10.32604/cmc.2024.050158 - 20 June 2024

    Abstract With the continuous advancement of China’s “peak carbon dioxide emissions and Carbon Neutrality” process, the proportion of wind power is increasing. In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine (IL-Bagging-DHKELM) error affinity propagation cluster analysis is proposed. The algorithm effectively combines deep hybrid kernel extreme learning machine (DHKELM) with incremental learning (IL). Firstly, an initial wind power prediction model is trained using the Bagging-DHKELM… More >

  • Open Access

    ARTICLE

    Analysis of CLARANS Algorithm for Weather Data Based on Spark

    Jiahao Zhang, Honglin Wang*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2427-2441, 2023, DOI:10.32604/cmc.2023.038462 - 30 August 2023

    Abstract With the rapid development of technology, processing the explosive growth of meteorological data on traditional standalone computing has become increasingly time-consuming, which cannot meet the demands of scientific research and business. Therefore, this paper proposes the implementation of the parallel Clustering Large Application based upon RANdomized Search (CLARANS) clustering algorithm on the Spark cloud computing platform to cluster China’s climate regions using meteorological data from 1988 to 2018. The aim is to address the challenge of applying clustering algorithms to large datasets. In this paper, the morphological similarity distance is adopted as the similarity measurement… More >

  • Open Access

    ARTICLE

    Conventional Breeding and Molecular Markers for Blast Disease Resistance in Rice (Oryza sativa L.)

    Adel A. Rezk1,2, Mohamed M. El-Malky3, Hossam S. El-Beltagi1,4,*, Mohammed Al-daej1, Kotb A. Attia5,6

    Phyton-International Journal of Experimental Botany, Vol.92, No.3, pp. 725-746, 2023, DOI:10.32604/phyton.2022.024645 - 29 November 2022

    Abstract Monogenic lines, which carried 23 genes for blast resistance were tested and used donors to transfer resistance genes by crossing method. The results under blast nursery revealed that 9 genes from 23 genes were susceptible to highly susceptible under the three locations (Sakha, Gemmeza, and Zarzoura in Egypt); Pia, Pik, Pik-p, Piz-t, Pita, Pi b, Pi, Pi 19 and Pi 20. While, the genes Pii, Pik-s, Pik-h, Pi z, Piz-5, Pi sh, Pi 3, Pi 1, Pi 5, Pi 7, Pi 9, Pi 12, Pikm and Pita-2 were highly resistant at the same locations. Clustering analysis confirmed the results, which divided into two groups; the first one included all the susceptible genes,… More >

  • Open Access

    ARTICLE

    gscaLCA in R: Fitting Fuzzy Clustering Analysis Incorporated with Generalized Structured Component Analysis

    Ji Hoon Ryoo1,*, Seohee Park2, Seongeun Kim3, Heungsun Hwang4

    CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 801-822, 2022, DOI:10.32604/cmes.2022.019708 - 27 June 2022

    Abstract Clustering analysis identifying unknown heterogenous subgroups of a population (or a sample) has become increasingly popular along with the popularity of machine learning techniques. Although there are many software packages running clustering analysis, there is a lack of packages conducting clustering analysis within a structural equation modeling framework. The package, gscaLCA which is implemented in the R statistical computing environment, was developed for conducting clustering analysis and has been extended to a latent variable modeling. More specifically, by applying both fuzzy clustering (FC) algorithm and generalized structured component analysis (GSCA), the package gscaLCA computes membership prevalence and… More >

  • Open Access

    ARTICLE

    RAIM Algorithm Based on Fuzzy Clustering Analysis

    Shouzhou Gu1,*, Jinzhong Bei1, Chuang Shi2, Yaming Dang1, Zuoya Zheng4, Congcong Cui5

    CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 281-293, 2019, DOI:10.32604/cmes.2019.04421

    Abstract With the development of various navigation systems (such as GLONASS, Galileo, BDS), there is a sharp increase in the number of visible satellites. Accordingly, the probability of multiply gross measurements will increase. However, the conventional RAIM methods are difficult to meet the demands of the navigation system. In order to solve the problem of checking and identify multiple gross errors of receiver autonomous integrity monitoring (RAIM), this paper designed full matrix of single point positioning by QR decomposition, and proposed a new RAIM algorithm based on fuzzy clustering analysis with fuzzy c-means (FCM). And on More >

  • Open Access

    ARTICLE

    Research on the Clustering Analysis and Similarity in Factor Space

    Sha-Sha Li1,2,∗, Tie-Jun Cui1,2,3,†, Jian Liu1,2,‡

    Computer Systems Science and Engineering, Vol.33, No.5, pp. 397-404, 2018, DOI:10.32604/csse.2018.33.397

    Abstract In this paper, we study the in uence of multiple domain attributes on the clustering analysis of object based on factor space. The representation method of graphical domain attribute is proposed for the object, which is called attribute circle. An attribute circle can represent infinite domain attributes. The similarity analysis of objects is first based on the concept of attribute circle, and the definition of graphical similarity is transformed into the definition of numerical similarity, and then the clustering analysis method of object set is studied and improved. Considering three kinds of graphical overlap, the… More >

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