Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics ...Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics of edge networks,such as limited resources,complex network faults,and the need for high real-time performance,enhancing and optimizing existing network fault diagnosis methods is necessary.Therefore,this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network(LSNN).Firstly,we use the Izhikevich neurons model to replace the Leaky Integrate and Fire(LIF)neurons model in the LSNN model.Izhikevich neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data inputs.Inspired by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of FSIs.Secondly,inspired by the connection mode based on spiking dynamics in the basal ganglia(BG)area of the brain,we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy consumption.Furthermore,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN model.Experiments on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis dimensions.Simultaneously,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model efficiency.To conclude,experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.展开更多
The Gierer-Meinhardt reaction-diffusion model is analyzed using a spectralcollocation method. This reaction-diffusion system is governed by activator and inhibitorconcentrations. Initially, the system is considered in...The Gierer-Meinhardt reaction-diffusion model is analyzed using a spectralcollocation method. This reaction-diffusion system is governed by activator and inhibitorconcentrations. Initially, the system is considered in one dimension and thenin two dimensions;numerical results are presented for both cases. The algorithmiccomplexity and accuracy are compared to those of a moving finite element method.Finally, observations are made concerning when to use the proposed spectral methodas opposed to the established moving mesh method.展开更多
基金supported by National Key R&D Program of China(2019YFB2103202).
文摘Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network management.Considering the unique characteristics of edge networks,such as limited resources,complex network faults,and the need for high real-time performance,enhancing and optimizing existing network fault diagnosis methods is necessary.Therefore,this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network(LSNN).Firstly,we use the Izhikevich neurons model to replace the Leaky Integrate and Fire(LIF)neurons model in the LSNN model.Izhikevich neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data inputs.Inspired by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of FSIs.Secondly,inspired by the connection mode based on spiking dynamics in the basal ganglia(BG)area of the brain,we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy consumption.Furthermore,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN model.Experiments on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis dimensions.Simultaneously,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model efficiency.To conclude,experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.
基金the NSF Grant DMS-0453600.Special thanks are due to the Hong Kong Baptist University for their generous accommodations,specifically Dr.Tao Tang for his time and support.Also we would like to thank Dr.Graeme Fairweather for his guidance throughout this project,and Dr.Greg Fasshauer for his suggestions and contributions.
文摘The Gierer-Meinhardt reaction-diffusion model is analyzed using a spectralcollocation method. This reaction-diffusion system is governed by activator and inhibitorconcentrations. Initially, the system is considered in one dimension and thenin two dimensions;numerical results are presented for both cases. The algorithmiccomplexity and accuracy are compared to those of a moving finite element method.Finally, observations are made concerning when to use the proposed spectral methodas opposed to the established moving mesh method.