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RISS 인기검색어
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- 저자 : Minte Zhang (검색결과 3 건)
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Self-Organized Synthesis and Mechanism of SnO2@Carbon Tube-Core Nanowire
Minting Luo,Yongjun Ma,Chonghua Pei,Yujing Xing,Lixia Wen,Li Zhang 대한화학회 2012 Bulletin of the Korean Chemical Society Vol.33 No.8
SnO2@carbon tube-core nanowire was synthesized via a facile self-organized method, which was in situ by one step via Chemical Vapor Deposition. The resulting composite was characterized by scanning electron microscopy, X-ray diffraction and transmission electron microscope. The diameter of the single nanowire is between 5 nm and 60 nm, while the length would be several tens to hundreds of micrometers. Then X-ray diffraction pattern shows that the composition is amorphous carbon and tin dioxide. Transmission electron microscope images indicate that the nanowire consists of two parts, the outer carbon tube and the inner tin dioxide core. Meanwhile, the possible growth mechanism of SnO2@carbon tube-core nanowire is also discussed.
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Self-Organized Synthesis and Mechanism of SnO<sub>2</sub>@Carbon Tube-Core Nanowire
Luo, Minting,Ma, Yong-Jun,Pei, Chonghua,Xing, Yujing,Wen, Lixia,Zhang, Li Korean Chemical Society 2012 Bulletin of the Korean Chemical Society Vol.33 No.8
$SnO_2@carbon$ tube-core nanowire was synthesized via a facile self-organized method, which was in situ by one step via Chemical Vapor Deposition. The resulting composite was characterized by scanning electron microscopy, X-ray diffraction and transmission electron microscope. The diameter of the single nanowire is between 5 nm and 60 nm, while the length would be several tens to hundreds of micrometers. Then X-ray diffraction pattern shows that the composition is amorphous carbon and tin dioxide. Transmission electron microscope images indicate that the nanowire consists of two parts, the outer carbon tube and the inner tin dioxide core. Meanwhile, the possible growth mechanism of $SnO_2@carbon$ tube-core nanowire is also discussed.
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Vibration-based structural health monitoring using CAE-aided unsupervised deep learning
Tong Guo,Minte Zhang,Ruizhao Zhu,Yueran Zong,Zhihong Pan 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.30 No.6
Vibration-based structural health monitoring (SHM) is crucial for the dynamic maintenance of civil building structures to protect property security and the lives of the public. Analyzing these vibrations with modern artificial intelligence and deep learning (DL) methods is a new trend. This paper proposed an unsupervised deep learning method based on a convolutional autoencoder (CAE), which can overcome the limitations of conventional supervised deep learning. With the convolutional core applied to the DL network, the method can extract features self-adaptively and efficiently. The effectiveness of the method in detecting damage is then tested using a benchmark model. Thereafter, this method is used to detect damage and instant disaster events in a rubber bearing-isolated gymnasium structure. The results indicate that the method enables the CAE network to learn the intact vibrations, so as to distinguish between different damage states of the benchmark model, and the outcome meets the high-dimensional data distribution characteristics visualized by the t-SNE method. Besides, the CAE-based network trained with daily vibrations of the isolating layer in the gymnasium can precisely recover newly collected vibration and detect the occurrence of the ground motion. The proposed method is effective at identifying nonlinear variations in the dynamic responses and has the potential to be used for structural condition assessment and safety warning.
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