Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (2)
  • Open Access


    Algorithm Selection Method Based on Coupling Strength for Partitioned Analysis of Structure-Piezoelectric-Circuit Coupling

    Daisuke Ishihara*, Naoto Takayama

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1237-1258, 2024, DOI:10.32604/cmes.2023.030211

    Abstract In this study, we propose an algorithm selection method based on coupling strength for the partitioned analysis of structure-piezoelectric-circuit coupling, which includes two types of coupling or inverse and direct piezoelectric coupling and direct piezoelectric and circuit coupling. In the proposed method, implicit and explicit formulations are used for strong and weak coupling, respectively. Three feasible partitioned algorithms are generated, namely (1) a strongly coupled algorithm that uses a fully implicit formulation for both types of coupling, (2) a weakly coupled algorithm that uses a fully explicit formulation for both types of coupling, and (3) a partially strongly coupled and… More >

  • Open Access


    A Learning Framework for Intelligent Selection of Software Verification Algorithms

    Weipeng Cao1, Zhongwu Xie1, Xiaofei Zhou2, Zhiwu Xu1, Cong Zhou1, Georgios Theodoropoulos3, Qiang Wang3,*

    Journal on Artificial Intelligence, Vol.2, No.4, pp. 177-187, 2020, DOI:10.32604/jai.2020.014829

    Abstract Software verification is a key technique to ensure the correctness of software. Although numerous verification algorithms and tools have been developed in the past decades, it is still a great challenge for engineers to accurately and quickly choose the appropriate verification techniques for the software at hand. In this work, we propose a general learning framework for the intelligent selection of software verification algorithms, and instantiate the framework with two state-of-the-art learning algorithms: Broad learning (BL) and deep learning (DL). The experimental evaluation shows that the training efficiency of the BL-based model is much higher than the DL-based models and… More >

Displaying 1-10 on page 1 of 2. Per Page