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RISS 인기검색어
- 검색키워드
- 저자 : Li Zhufeng (검색결과 3 건)
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Li Zhufeng,Gao Jie,Yang Yuwei 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.3
With the advancement of technology and human movement towards evolution, intelligent control methods are becoming more important. One of the areas of progress is related to the development of new frameworks for electricity generation and distribution systems, and the microgrid structure with economic capabilities is one of the frameworks. Accordingly, this paper presents a new practical method for controlling the frequency of microgrids and is able to cover the following issues at the same time. 1- It considers the nonlinear model of provisional microgrid which has a hybrid structure (AC and DC) in addition to renewable energy sources. 2- Introduces a method for microgrid frequency control under different operational conditions that performs based on the brain emotional learning. 3- Ensures the operation and applicability of the control method for the provisional microgrid through implementation of FPGA for the first time 4- Confirms the robustness of the proposed method under extreme load changes. So, in the simulation scenarios, the effects of wind turbine and solar energy are considered and under the influence of various changes in load and system uncertainties, the robustness and efficiency of the proposed method are well demonstrated. With the advancement of technology and human movement towards evolution, intelligent control methods are becoming more important. One of the areas of progress is related to the development of new frameworks for electricity generation and distribution systems, and the microgrid structure with economic capabilities is one of the frameworks. Accordingly, this paper presents a new practical method for controlling the frequency of microgrids and is able to cover the following issues at the same time. 1- It considers the nonlinear model of provisional microgrid which has a hybrid structure (AC and DC) in addition to renewable energy sources. 2- Introduces a method for microgrid frequency control under diferent operational conditions that performs based on the brain emotional learning. 3- Ensures the operation and applicability of the control method for the provisional microgrid through implementation of FPGA for the frst time 4- Confrms the robustness of the proposed method under extreme load changes. So, in the simulation scenarios, the efects of wind turbine and solar energy are considered and under the infuence of various changes in load and system uncertainties, the robustness and efciency of the proposed method are well demonstrated.
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Craze density based fatigue-damage analysis in polyethylene methacrylate
Zongzhan Gao,Wei Liu,Qinghai Li,Shiling Liu,Zhufeng Yue,Baoxing Xu 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.1
The S-N curve, also known as the Wöhler curve, is well acknowledged and widely used in the prediction of fatigue life of engineering materials. In this study, we present a craze density model, as an alternative approach, to predict the fatigue life of polymer materialpolyethylene methacrylate (PMMA). Our experiments show that craze grows rapidly with the increase of fatigue loadings after their initiation on the surface of PMMA till to the failure of specimens. Dynamic measurements indicate that the growth rate of craze density reaches a stable stage after a rapid accumulation at the beginning, and dominates the fatigue life of PMMA. Both initiation time of crazing and deformation energy of PMMA are probed through the recorded fatigue stress-strain curves and the optical microscope (OM) observations on crazing evolutions. The critical growth rate of the craze density is correlated with the yield stress and strain of PMMA at quasi-static loadings. On the basis of the craze density, an experimental model is established to predict the fatigue damage and life of PMMA. The predication shows good agreement with that from both experiments and traditional S-N curves in a broad range of fatigue loadings.
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Paper Recommendation Using SPECTER with Low-Rank and Sparse Matrix Factorization
Panpan Guo,Gang Zhou,Jicang Lu,Zhufeng Li,Taojie Zhu 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.5
With the sharp increase in the volume of literature data, researchers must spend considerable time and energy locating desired papers. A paper recommendation is the means necessary to solve this problem. Unfortunately, the large amount of data combined with sparsity makes personalizing papers challenging. Traditional matrix decomposition models have cold-start issues. Most overlook the importance of information and fail to consider the introduction of noise when using side information, resulting in unsatisfactory recommendations. This study proposes a paper recommendation method (PR-SLSMF) using document-level representation learning with citation-informed transformers (SPECTER) and low-rank and sparse matrix factorization; it uses SPECTER to learn paper content representation. The model calculates the similarity between papers and constructs a weighted heterogeneous information network (HIN), including citation and content similarity information. This method combines the LSMF method with HIN, effectively alleviating data sparsity and cold-start issues and avoiding topic drift. We validated the effectiveness of this method on two real datasets and the necessity of adding side information.
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