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EFFECT OF Cu ATOMIC SEGREGATION ON THE FROZEN STRUCTURES OF Co–Cu BIMETALLIC CLUSTERS
YINGJIE ZHANG,YONGQIANG LI,XUYANG XIAO,YUNHUI YAN 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2012 NANO Vol.7 No.6
Atomic segregation in bimetallic clusters can in°uence the surface constituent and be used to a®ect the frozen structure. In this study, molecular dynamics simulation with an embedded atom method was used to study the frozen structures of (CoCu)561 clusters with di®erent Co contents. It is found that the clusters can freeze to form icosahedron, truncated octahedron, decahedron or hcp with the change of Co contents. In these geometries, the structure of the lowest energy state is hcp, then in turn decahedron and truncated octahedron. The frozen structures are related to the release of excess energy, while the released excess energy was a®ected by the amount of segregated Cu atoms. This means that the atomic segregation can be used to tune the structures of bimetallic clusters.
Yingjie Zhang,Yuan Jiang,Yan Chen,Ying Zhang 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.1
The high voltage circuit breaker’s fault as an important form of electrical contact fault in the power system, which is extremely difcult to diagnose under the condition of small fault dataset. This paper proposes a fault diagnosis method based on multiclassifcation relevance vector machine for high voltage circuit breakers. To make up with the scarcity of the sample fault data in classifying the features of the high voltage circuit breakers, a multi-classifcation relevance vector machine algorithm is designed on the basis of “One-Against-One” multi-classifcation model, and tested by public data-sets to verify the good generating performance of this algorithm on small sample data-sets. Then, the time and the currents features are extracted from the closing coil current information of the high voltage circuit breaker to form fault eigenvector. Consequently, numerous two-classifcation relevance vector machine models were trained and then tested for the optimality of the acquired parameters. The results show that the proposed algorithm can efectively identify many faults of circuit breaker and has better classifcation accuracy than BP neural network and Support Vector Machine under conditions of small sample data.
Yingjie Sun,Pin Zhang,Hang Zheng,Luna Dong,Lei Tan,Cuiping Song,Xusheng Qiu,Ying Liao,Chunchun Meng,Shengqing Yu,Chan Ding 대한수의학회 2018 Journal of Veterinary Science Vol.19 No.1
T-cell internal antigen-1 (TIA-1) has roles in regulating alternative pre-mRNA splicing, mRNA translation, and stress granule (SG) formation in human cells. As an evolutionarily conserved response to environmental stress, SGs have been reported in various species. However, SG formation in chicken cells and the role of chicken TIA-1 (cTIA-1) in SG assembly has not been elucidated. In the present study, we cloned cTIA-1 and showed that it facilitates the assembly of canonical SGs in both human and chicken cells. Overexpression of the chicken prion-related domain (cPRD) of cTIA-1 that bore an N-terminal green fluorescent protein (GFP) tag (pntGFP-cPRD) or Flag tag (pFlag-cPRD) induced the production of typical SGs. However, C-terminal GFP-tagged cPRD induced notably large cytoplasmic granules that were devoid of endogenous G3BP1 and remained stable when exposed to cycloheximide, indicating that these were not typical SGs, and that the pntGFP tag influences cPRD localization. Finally, endogenous cTIA-1 was recruited to SGs in chicken cells and tissues under environmental stress. Taken together, our study provide evidence that cTIA-1 has a role in canonical SG formation in chicken cells and tissues. Our results also indicate that cPRD is necessary for SG aggregation.
Yuechao Zhang,Yingjie Li,Junjie Gu,Senlin Tian,Ping Ning 한국화학공학회 2018 Korean Journal of Chemical Engineering Vol.35 No.9
Supercritical water (SCW) impregnation is an efficient and feasible method that has been used to prepare highly dispersed supported catalysts, but few studies have investigated the stability of support materials in supercritical water. Thus, our aim was to investigate the hydrothermal stability of zeolite supports (ZSM-5, TS-1, ZSM-35, HY, 13X, Beta, SAPO-11 and SAPO-34) as model compounds in supercritical water. Results showed that almost all of zeolites suffered from crystallinity change, structural properties degradation, obvious desilication and dealumination. The decrease of surface areas and the collapse of crystalline structures in HY, 13X, Beta, SAPO-11 and SAPO-34 were more serious compared to ZSM-5, ZSM-35 and TS-1. The micropore areas and acidity of all SCW-treated zeolites were reduced. 13X with lower Si/Al ratio had higher hydrothermal stability than HY due to the formation of extra-framework Al (EFAL). EFAl also generated strong Lewis acid sites determined by ammonia temperature-programmed desorption and 27Al magic angle spinning nuclear magnetic resonance. Desilication and dealumination were simultaneous, and led to the increase of framework Si/Al ratio. ZSM zeolites (ZSM-5, ZSM-35 and TS-1) had higher hydrothermal stability than HY, 13X, Beta, SAPO-11 and SAPO-34 in SCW.
Neural network adaptive position tracking control of underactuated autonomous surface vehicle
ChengJu Zhang,Cong Wang,Yingjie Wei,JinQiang Wang 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.2
The present study investigates the position tracking control of the underactuated autonomous surface vehicle, which is subjected to parameters uncertainties and external disturbances. In this regard, the backstepping method, neural network, dynamic surface control and the sliding mode method are employed to design an adaptive robust controller. Moreover, a Lyapunov synthesis is utilized to verify the stability of the closed-loop control system. Following innovations are highlighted in this study: (i) The derivatives of the virtual control signals are obtained through the dynamic surface control, which overcomes the computational complexities of the conventional backstepping method. (ii) The designed controller can be easily applied in practical applications with no requirement to employ the neural network and state predictors to obtain model parameters. (iii) The prediction errors are combined with position tracking errors to construct the neural network updating laws, which improves the adaptation and the tracking performance. The simulation results demonstrate the effectiveness of the proposed position tracking controller.