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RISS 인기검색어
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- 저자 : Peng-cheng Nie (검색결과 2 건)
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Chen, Jing,Nie, Hai,Peng, Cheng,Qi, Shibin,Tang, Chaojun,Zhang, Ying,Wang, Lianhui,Park, Gun-Sik IEEE 2018 Journal of Lightwave Technology Vol.36 No.16
<P>We report an effective method to enhance and modify the magnetic plasmon (MP) resonance in three-dimensional (3D) optical metamaterials consisting of periodic arrays of silver vertical split-ring resonators (VSRRs) for high-sensitivity sensing. By positioning the 3D metamaterials above a thick silver film separated by a silica dielectric spacer layer, the strong coupling between the MP resonance in the VSRRs and the surface plasmons polaritons (SPPs) propagating on the silver film can be realized and gives rise to an ultra-narrowband hybrid MP mode with a huge enhancement of magnetic fields. For the coupling to happen, the magnetic field direction of the SPPs should be parallel to the magnetic moment induced in the VSRRs. More importantly, because the ultra-narrowband hybrid MP mode is extremely sensitive to the surrounding media, the sensitivity and the figure of merit (FOM) of the 3D metamaterials can reach as high as 700 nm/RIU and 170, respectively, suggesting that the proposed 3D metamaterials hold potential applications in label-free biomedical sensing.</P>
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Data-driven prognostics method for turbofan engine degradation using hybrid deep neural network
Bin Xue,Zhong-bin Xu,Xing Huang,Peng-cheng Nie 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.12
Powerful sequence modeling capability for massive multi-sensor data enables deep-learning-based methods to obtain accurate remaining useful life (RUL) estimations. Hybrid neural networks, with learned representations based on various networks, have enhanced the prognostics accuracies than single networks. However, assembly strategies that are limited to either parallel or serial, and insufficient utilization of single networks restrict the development of hybrid networks for more complex problems. This paper proposes a datadriven method using hybrid multi-scale convolutional neural network (MSCNN) and bidirectional long short-term memory (BLSTM) network (namely HMCB network) for RUL estimation. The framework of the network includes two parallel paths. One is composed of MSCNN and BLSTM in serial and the other is a BLSTM path. The HMCB network integrates the merits of multi-scale spatial feature extraction of MSCNN and sequence learning capacity of BLSTM. Validated by C-MAPSS dataset, the HMCB network demonstrates noticeably higher prognostic accuracy than other state-of-the-art methods.
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