Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (63)
  • Open Access

    ARTICLE

    Ensemble Recurrent Neural Network-Based Residual Useful Life Prognostics of Aircraft Engines

    Jun Wu1,*, Kui Hu1, Yiwei Cheng2, Ji Wang1, Chao Deng2,*, Yuanhan Wang3

    Structural Durability & Health Monitoring, Vol.13, No.3, pp. 317-329, 2019, DOI:10.32604/sdhm.2019.05571

    Abstract Residual useful life (RUL) prediction is a key issue for improving efficiency of aircraft engines and reducing their maintenance cost. Owing to various failure mechanism and operating environment, the application of classical models in RUL prediction of aircraft engines is fairly difficult. In this study, a novel RUL prognostics method based on using ensemble recurrent neural network to process massive sensor data is proposed. First of all, sensor data obtained from the aircraft engines are preprocessed to eliminate singular values, reduce random fluctuation and preserve degradation trend of the raw sensor data. Secondly, three kinds of recurrent neural networks (RNN),… More >

  • Open Access

    ARTICLE

    Defense Against Poisoning Attack via Evaluating Training Samples Using Multiple Spectral Clustering Aggregation Method

    Wentao Zhao1, Pan Li1,*, Chengzhang Zhu1,2, Dan Liu1, Xiao Liu1

    CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 817-832, 2019, DOI:10.32604/cmc.2019.05957

    Abstract The defense techniques for machine learning are critical yet challenging due to the number and type of attacks for widely applied machine learning algorithms are significantly increasing. Among these attacks, the poisoning attack, which disturbs machine learning algorithms by injecting poisoning samples, is an attack with the greatest threat. In this paper, we focus on analyzing the characteristics of positioning samples and propose a novel sample evaluation method to defend against the poisoning attack catering for the characteristics of poisoning samples. To capture the intrinsic data characteristics from heterogeneous aspects, we first evaluate training data by multiple criteria, each of… 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 >

Displaying 61-70 on page 7 of 63. Per Page