To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was *** chaotic elite opposition-based l...
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To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was *** chaotic elite opposition-based learning process was applied to initialize the entire population,which enhanced the quality of the initial individuals and the population diversity,made the initial individuals distribute in the better quality areas,and accelerated the search efficiency of the *** inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm,and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local *** LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics,and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability,search accuracy and convergence *** addition,the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.
Convolutional neural networks are widely used in computer vision and image processing. However, when the original input is added with manually imperceptible perturbations, these deep network models mostly tend to outp...
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There is an increasingly urgent need to develop cost-effective electrocatalysts with high catalytic activity and stability as alternatives to the traditional Pt/C in catalysts in water *** this study,microspheres comp...
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There is an increasingly urgent need to develop cost-effective electrocatalysts with high catalytic activity and stability as alternatives to the traditional Pt/C in catalysts in water *** this study,microspheres composed of Mo-doped NiCoP nanoneedles supported on nickel foam were prepared to address this *** results show that the nanoneedles provide sufficient active sites for efficient electron transfer;the small-sized effect and the micro-scale roughness enhance the entry of reactants and the release of hydrogen bubbles;the Mo doping effectively improves the electrocatalytic performance of NiCoP in alkaline *** catalyst exhibits low hydrogen evolution overpotentials of 38.5 and 217.5 mV at a current density of 10 mA·cm^(-2) and high current density of 500 mA·cm^(-2),respectively,and only 1.978 V is required to achieve a current density of 1000 mA·cm^(-2) for overall water *** functional theory(DFT)calculations show that the improved hydrogen evolution performance can be explained as a result of the Mo doping,which serves to reduce the interaction between NiCoP and intermediates,optimize the Gibbs free energy of hydrogen adsorption(△G_(*H)),and accelerate the desorption rate of **** study provides a promising solution to the ongoing challenge of designing efficient electrocatalysts for high-current-density hydrogen production.
The internet of things (IoT) is a complex network system with applications in all walks of life. However, there are various risks in the process of information transmission between IoT devices and servers. Recently, r...
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In recent years, HPC and AI fusion, which refers to applying AI technology to traditional HPC applications, has become a new trend. HPC and AI fusion requires supports for multiple precisions used in both domains. Whi...
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Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby n...
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Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.
Most of the images on the Internet are color images, and steganalysis of color images is a very critical issue in the field of steganalysis. The current proposed color image steganalysis features mainly rely on manual...
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The classical computer is restricted by Moore's law and other factors into the development bottleneck, quantum computer by virtue of its natural acceleration advantage into the field of vision. Quantum computing c...
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In the research of imbalanced data classification, the resampling effect of the existing resampling and random forest combination technology is greatly affected by the characteristic dimension of the training set, res...
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With the increasing number of cyber-attack behaviors, it has caused great harm to the contemporary society. Currently, the cyber attack behavior recognition module is usually implemented by adopting a strategy based o...
ISBN:
(纸本)9798400716485
With the increasing number of cyber-attack behaviors, it has caused great harm to the contemporary society. Currently, the cyber attack behavior recognition module is usually implemented by adopting a strategy based on rule-base matching, which often fails to identify the attack behaviors when faced with unpreset path attack behaviors of the actual attack process due to its reliance on predefined abnormal behavior patterns and attack labels. Therefore, this paper proposes an efficient and accurate unpreset behavior detection framework for cyber-attack behaviors, introduces machine learning techniques into the identification of unpreset attack behaviors, proposes an RF-Corr feature importance assessment method based on the Kendall correlation coefficient between features and the feature weight values, and designs an unpreset attack behavior identification method based on BiLSTM neural network, which improves the current effectiveness of identification and detection for unpresetted behavior of cyber attacks, and provides a solution for artificial intelligence detection of unpresetted behavior of cyber attacks.
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