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- 저자 : Jinchao Li (검색결과 3 건)
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Hongbin Li,Wenying Shi,Shuo Mei,Jinchao Li,Qiyun Du,Longwei Qin,Haixia Zhang 한국섬유공학회 2019 Fibers and polymers Vol.20 No.2
Poly(p-phenylene terephthalamide) (PPTA), as the raw polymer material of Aramid fiber-1414, has high modulus and high strength which is commonly used in the reinforced composites. However, the general blending often accompany the microphase incompatibility in polymer matrix. In this study, PPTA molecules were incorporated into poly(vinylidene fluoride) (PVDF) membrane matrix through in situ polycondensation of p-phenylene diamine (PPD) and terephthaloyl chloride (TPC) in PVDF solution and PPTA/PVDF molecular composite membranes were prepared via the subsequent nonsolvent induced phase separation (NIPS) process. The anti-compaction properties of resulted PPTA/PVDF molecular composite membranes were systematically investigated including the variations of bursting pressure and membrane thickness, porosity, pure water flux under different pressures. The results suggested that membrane comprehensive anticompaction properties were improved with the increasing of PPTA content in PVDF membrane matrix. Combining the variation trends of membrane anti-compaction properties with morphologies, the compaction-deformation evolution processes of two membrane pore types (sponge-like and finger-like pores) were proposed at the end.
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Hu Jinchao,Li Xu,Hu Weiming,Xu Qimin,Huyue Sun 한국자동차공학회 2023 International journal of automotive technology Vol.24 No.6
Decision-making is the “brain” of connected and automated vehicles (CAVs) and is vitally critical to the safety of CAVs. The most of driving data used to train the decision-making algorithms is collected in general traffic conditions. Existing decision-making methods are difficult to guarantee safety in challenging traffic conditions, namely severe congestion and accident ahead. In this context, a semi-supervised decision-making algorithm is proposed to improve the safety of CAVs in challenging traffic conditions. To be specific, we proposed the expert-generative adversarial imitation learning (E-GAIL) that integrates imitation learning and deep reinforcement learning. The proposed E-GAIL is deployed in roadside unit (RSU). In the first stage, the decision-making knowledge of the expert is imitated using the real-world data collected in general traffic conditions. In the second stage, the generator of E-GAIL is further reinforced and achieves self-learn decision-making in the simulator with challenging traffic conditions. The E-GAIL is tested in general and challenging traffic conditions. By comparing the evaluation metrics of time to collision (TTC), deceleration to avoid a crash (DRAC), space gap (SGAP) and time gap (TGAP), the E-GAIL greatly outperforms the state-of-the-art decision-making algorithms. Experimental results show that the E-GAIL not only make-decision for CAVs in general traffic conditions but also successfully enhances the safety of CAVs in challenging traffic conditions.
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Namkha Norbu,Yuegao Liu,Jinchao Li,Qunzi Jia,Wei Wang,Xiaobing Song,Yazhi Li,Huilei Kong,Yijie Gao,Xianzheng Guo,Xuegang Yao 한국지질과학협의회 2021 Geosciences Journal Vol.25 No.6
The East Kunlun orogenic belt (EKOB) is located in the northeastern part of the Qinghai-Tibetan Plateau, China. Voluminous Silurian–Devonian granitoids were formed during the Proto-Tethys Ocean subduction and collision process. In this study, zircon U-Pb age dating, in-situ Hf isotope analyses, and whole rock major and trace element analyses have been carried out on granitoids from the central to eastern EKOB. The formation ages of granodiorite in the Bairiqili area, the granodiorite in the Wulonggou area are 410 ± 2 Ma and 396 ± 1 Ma, respectively, and the granodiorites show I-type features. The Wulonggou syenogranite with a formation age of 398 ± 2 Ma has a high differentiation index (D.I. = 89.59–92.43), and may belong to the high fractionated I-type granite. The Lalangmai tonalite with a formation age of 423 ± 2 Ma has high Sr (339–590 μg/g), εHf(t) (1.5–7.3), and low Y (3.45–6.33 μg/g), which are similar to modern adakites. The Lalangmai tonalite is the latest adakite record during the Proto-Tethys evolution in EKOB. Combined with previous studies, it is concluded that the granitoids from Cambrian to Devonian divided into four episodes. The first episode (515–436 Ma) mainly consists of I-type island arc granites and adakite, which are formed in a subduction environment. The second episode (436–427 Ma) is characterized by the S-type granites related to the collision. The third episode (427–420 Ma) is the transition period between the collision and the post-collision environment. In the fourth episodes (420–385 Ma), granitoids were formed in post-collision extensional environment, which is characterized by I-type and A-type granites. The upwelling of asthenosphere material is likely caused by the collapse of the orogenic belt. After 420 Ma, I-type and A-type granites formed at different depths but in the same environment.
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