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  • Open Access


    Coal Rock Condition Detection Model Using Acoustic Emission and Light Gradient Boosting Machine

    Jing Li1, Yong Yang2, *, Hongmei Ge1, Li Zhao3, Ruxue Guo3, 4

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 151-162, 2020, DOI:10.32604/cmc.2020.05649

    Abstract Coal rock mass instability fracture may result in serious hazards to underground coal mining. Acoustic emissions (AE) stimulated by internal structure fracture should carry lots of favorable information about health condition of rock mass. AE as a sensitive non-destructive test method is gradually utilized to detect anomaly conditions of coal rock. This paper proposes an improved multi-resolution feature to extract AE waveform at different frequency resolutions using Coilflet Wavelet Transform method (CWT). It is further adopt an efficient Light Gradient Boosting Machine (LightGBM) by several cascaded sub weak classifier models to merge AE features at More >

  • Open Access


    Intelligent Spectrum Detection Model Based on Compressed Sensing in Cognitive Radio Network

    Yanli Ji1, *, Weidong Wang2, Yinghai Zhang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 691-701, 2020, DOI:10.32604/cmes.2020.07861

    Abstract In view of the uncertainty of the status of primary users in cognitive networks and the fact that the random detection strategy cannot guarantee cognitive users to accurately find available channels, this paper proposes a joint random detection strategy using the idle cognitive users in cognitive wireless networks. After adding idle cognitive users for detection, the compressed sensing model is employed to describe the number of available channels obtained by the cognitive base station to derive the detection performance of the cognitive network at this time. Both theoretical analysis and simulation results show that using More >

  • Open Access


    A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data

    Xiaonian Wu1,*, Chuyun Zhang3, Runlian Zhang2, Yujue Wang2, Jinhua Cui4

    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 61-72, 2018, DOI: 10.3970/cmc.2018.02449

    Abstract There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model More >

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