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RISS 인기검색어
- 검색키워드
- 저자 : Yanglong Li (검색결과 3 건)
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Mathematical Modelling of Online Warping Height of Cold-Rolled Thin Strip Steel
Yanglong Li,Jie Wen,Haihai Lin,Meng Yu,Fengqin Wang 한국강구조학회 2022 International Journal of Steel Structures Vol.22 No.4
The warping deformation is a common shape defect for thin strip steel. The warping form is shown as type-C for online strip with tension, whereas it is generally type-L for offl ine steel sheet without tension. A mathematical model between type- C warping height for online strip and type-L warping height for offl ine steel sheet was established and validated by fi eld experiments. The predicted offl ine warping height was agreed well with the measured results, and the average relative error was about 5.4%. There is a linear relationship between online and offl ine warping heights. The infl uence factors on online warping height were also discussed. At the same offl ine warping value of steel sheet, the online warping value increases with the increase of warping correction factor and the decrease of the sampling sheet length. The online warping height is also aff ected by width and Poisson’s ratio of strip. This model could be used to predict the online warping height of steel strip based on the offl ine warping height of steel sheet. Also the offl ine warping height can be evaluated according to the online warping state. It lays a primary theoretical foundation for the warping relationship between online strip and offl ine steel sheet.
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Robot Search Path Planning Method Based on Prioritized Deep Reinforcement Learning
Yanglong Liu,Zuguo Chen,Ming Lu,Chaoyang Chen,Xuzhuo Zhang,Yonggang Li 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.8
The path planning process of the robot relies too much on environmental information, which makes it difficult to obtain the optimal search path when the search and rescue tasks are carried out in a complex postdisaster environment. Thus, a path planning method based on prioritized deep reinforcement learning is proposed in the paper. The core idea of the method is that the robot first builds an environment mathematical model based on the obtained information through the sensors. Then, to make the robot can obtain the optimal search policy in an extremely complex environment, the prioritized replay mechanism is used to improve deep reinforcement learning. Finally, the simulation results show that the search path planning method based on prioritized deep reinforcement learning proposed can not only improve the convergence speed of the model but also is endowed good robustness in this paper.
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Shape-controlled Synthesis and Magnetic Properties of FePt Nanocubes
Mingge Zhou,Wei Li,Minggang Zhu,Dong Zhou,Yanglong Hou 한국물리학회 2013 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.63 No.3
In this research, FePt nanocubes, octapods and polyhedra were successfully prepared withMo(Co)6 as a reducing agent. The chemically-synthesized cubic FePt nanoparticles could easilybe self-assembled into an oriented nanoarray. These ordered nanomagnet arrays were expected toachieve high-density information storage and high performance in permanent magnets. The selfassembedFePt nanocubes were chemically disordered with a face centered cubic (fcc) structure. During annealing, these particles changed to a face-centered tetragonal (fct) order. The phase structure,the micro-morphology and magnetic properties were characterized by using X-ray diffraction(XRD), High resolution transmission electron microscope (HRTEM), Scanning electron microscope(SEM) and vibrating sample magnetometer (VSM).
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