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RISS 인기검색어
- 검색키워드
- 저자 : Guo Xiuting (검색결과 2 건)
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Tong He,Yang Bai,Xiuting Liu,Dan Guo,Yandong Liu 대한금속·재료학회 2018 METALS AND MATERIALS International Vol.24 No.4
We investigated the Effect of Sn micro-alloying on recrystallization nucleation and growth processes of ferritic stainlesssteels. The as-received hot rolled sheets were cold rolled up to 80% reduction and then annealed at 740–880 °C for 5 min. The cold rolling and recrystallization microstructures and micro-textures of Sn-containing and Sn-free ferritic stainless steelswere all determined by electron backscatter diff raction. Our Results show that Sn micro-alloying has important Effects onrecrystallization nucleation and growth processes of ferritic stainless steels. Sn micro-alloying conduces to grain fragmentationin the deformation band, more fragmented grains are existed in Sn-containing cold rolled sheets, which provides moresites for recrystallization nucleation. Sn micro-alloying also promotes recrystallization process and inhibits the growth ofrecrystallized grains. The recrystallization nucleation and growth mechanism of Sn-containing and Sn-free ferritic stainlesssteels are both characterized by orientation nucleation and selective growth, but Sn micro-alloying promotes the formation ofγ-oriented grains. Furthermore, Sn micro-alloying contributes to the formation of Σ13b CSL boundaries and homogeneousγ-fi ber texture. Combining the results of microstructure and micro-texture, the formability of Sn-containing ferritic stainlesssteels will be improved to some extent.
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A New CIGWO-Elman Hybrid Model for Power Load Forecasting
Hao Jie,Zhu Changsheng,Guo Xiuting 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.2
Time series forecasting is a common task that needs to be implemented in many engineering applications. In this paper, for the power load forecasting problem, we explore the advantages of the grey wolf optimization (GWO) algorithm for Elman network optimization. To avoid model complexity, the structure of the Elman network is simplifi ed to improve its training effi ciency. Then, a chaotic sequence and random cosine function are introduced into the GWO algorithm. In addition, the updating methods of individual positions in the particle swarm optimization (PSO) algorithm and diff erential evolution (DE) algorithm are used as references for improving the GWO algorithm. The new chaotic cosine inertial weights GWO (CIGWO) algorithm is used to optimize the parameters of the Elman network, and the CIGWO-Elman network model is formed. Finally, CIGWO-Elman is applied to the actual load data of a city in eastern China to realize short-term power load prediction. The results show that the proposed model has better predictive accuracy and real-time performance than those of other methods
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