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RISS 인기검색어
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- 저자 : Aiguo Lin (검색결과 3 건)
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Lin Li,Aiguo Zhang 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.3
On demand resource forecasting in cloud computing is an crucial guarantee for achieving effective management of all virtualized resources and reducing data center energy consumption. According to single forecasting model cannot integrate all the valid information which leads to the decline in prediction accuracy. This paper proposed an optimal combination prediction model for cloud computing resource requirement. This model is based on generalized Dice coefficient and the induced ordered weighted geometric mean (IOWGA) operator, as well as improved Elman neural network and grey forecasting model. It is able to accurately reflect the random information and trend information in cloud computing load thus will enhance the overall prediction accuracy. The experiment results show this method is feasible and effective.
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Improved AQPSO Algorithm for Solving the Model of the Skin Effect Electric Heating System
Ding Li,Ding Xinghua,Ren Weina,Lin Aiguo 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.5
In view of the importance of the skin-eff ect electric heat tracing system of the submarine oil pipeline in heating and condensation reduction, it is necessary to start from the skin electric heating mechanism and construct its complete mathematical model to realize the effi cient operation of the system. Aiming at the problem of unknown parameters in the model of the skin-eff ect electric heat tracing system, an improved adaptive quantum behaviour particle swarm optimization algorithm (AQPSO) based on the quantum behaviour particle swarm optimization algorithm is proposed, in order to eff ectively identify the model parameters. In the algorithm design, in view of the high dependence of the basic QPSO algorithm on the shrinkage and expansion coeffi cients, the particle aggregation factor is introduced, and the shrinkage and expansion coeffi cients are redesigned. In view of the diff erent proportions of the fi tness value of the individual optimal position, the weight coeffi cient is introduced to construct the best position of the average weight to realize the improvement of the basic QPSO algorithm. The simulation results show that the AQPSO algorithm has better performance than QPSO in terms of convergence accuracy and robustness. This algorithm not only overcomes the inherent premature defects of particle swarms, but also improves the accuracy of the algorithm and obtains accurate model parameters. It can be seen that the improved adaptive quantum behaviour particle swarm algorithm has certain feasibility and eff ectiveness in the parameter identifi cation of Hammerstein model.
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Research on Deep-Sea Pipeline Tube Bundle Heating System
Li Ding,Xinghua Ding,Weina Ren,Yingying Mu,Aiguo Lin 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.6
In order to ensure the safety of deep-sea submarine pipelines, this paper focuses on the analysis of the axial temperature distribution model of the submarine pipeline and the distribution parameter circuit model of the tube bundle heating system. Combined with these two models, theoretical analysis shows that the heating eff ect of the tube bundle heating system depends on the distributed circuit parameters and power frequency of the system. In order to improve the heat tracing effi ciency, the power frequency needs to be adjusted according to the change of the load temperature, so the power supply frequency of the tube bundle heating system based on the Hammerstein model is optimized. Using a neuro-fuzzy algorithm, not only the theoretical values of heating power and power frequency are obtained, but also the drawbacks of determining the above parameters based on engineering experiments are avoided. Moreover, the pipeline heating is effi cient and stable, the dynamic response is fast, and the working condition is also well adapted.
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