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  • Open Access

    ARTICLE

    Differing Mapping using Ensemble of Metamodels for Global Variable-fidelity Metamodeling

    Qi Zhou1, Xinyu Shao1, Ping Jiang1,2, Longchao Cao1, Hui Zhou1, Leshi Shu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.106, No.5, pp. 323-355, 2015, DOI:10.3970/cmes.2015.106.323

    Abstract Computational simulation models with different fidelity have been widely used in complex systems design. However, running the most accurate simulation models tends to be very time-consuming and can therefore only be used sporadically, while incorporating less accurate, inexpensive models into the design process may result in inaccurate design alternatives. To make a trade-off between high accuracy and low expense, variable fidelity (VF) metamodeling approaches that aim to integrate information from both low fidelity (LF) and high-fidelity (HF) models have gained increasing popularity. In this paper, a Difference Mapping Framework using Ensemble of Metamodels (DMF-EM) for global VF metamodeling is proposed.… More >

  • Open Access

    ARTICLE

    A Novel Ensemble Learning Algorithm Based on D-S Evidence Theory for IoT Security

    Changting Shi1, *

    CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 635-652, 2018, DOI:10.32604/cmc.2018.03754

    Abstract In the last decade, IoT has been widely used in smart cities, autonomous driving and Industry 4.0, which lead to improve efficiency, reliability, security and economic benefits. However, with the rapid development of new technologies, such as cognitive communication, cloud computing, quantum computing and big data, the IoT security is being confronted with a series of new threats and challenges. IoT device identification via Radio Frequency Fingerprinting (RFF) extracting from radio signals is a physical-layer method for IoT security. In physical-layer, RFF is a unique characteristic of IoT device themselves, which can difficultly be tampered. Just as people’s unique fingerprinting,… More >

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