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RISS 인기검색어
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- 저자 : Zhao Jiangpeng (검색결과 4 건)
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Enhanced dielectric and nonohmic properties of SrTiO3-modified CaCu3Ti4O12 ceramics
Zhao Jiangpeng,Sun Li,Cao Ensi,Hao Wentao,Zhang Yongjia,Chen Jianbin,Ju Lin 한국물리학회 2022 Current Applied Physics Vol.36 No.-
Dielectric and nonohmic properties of CaCu3Ti4O12 (CCTO) ceramics can be modified by addition of SrTiO3 (STO) in different molar proportions which were fabricated by a modified sol-gel method. XRD results indicated that all modified ceramics showed mixed phase consisting of both CCTO and STO. SEM images and grain size distribution probability also presented the change of microstructure with the addition of STO. The dielectric loss of the CCTO/0.4STO ceramics sintered at 1000 ◦C can be lower than 0.02 in a wide frequency (1 kHz–10 kHz), especially at 1 kHz, the dielectric loss of this sample is as low as 0.012. Furthermore, excellent nonlinear I–V electrical characteristic (high breakdown voltage to 54.15 kV/cm for CCTO/0.4STO sintered at 1000 ◦C) was observed as well. All the results indicated that the addition of STO does improve the dielectric properties and nonohmic characteristics of CCTO ceramics dramatically.
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Jiangpeng Shu,Gaoyang Liu,Yanbo Niu,Weijian Zhao,Yuan-Feng Duan 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1
The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.
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An active learning method with difficulty learning mechanism for crack detection
Zhicheng Zhang,Jiangpeng Shu,Jun Li,Jiawei Zhang,Weijian Zhao,Yuanfeng Duan 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1
Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is asignificant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320 × 320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.
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Kai Wu,Jiangpeng Zhai,Jianyang Xue,Fangyuan Xu,Hongtie Zhao 사단법인 한국계산역학회 2019 Computers and Concrete, An International Journal Vol.23 No.5
A low-cycle loading experiment of 16 transfer column specimens was conducted to study the influence of parameters, likes the extension length of shape steel, the ratio of shape steel, the axial compression ratio and the volumetric ratio of stirrups, on the shear distribution between steel and concrete, the concrete damage state and the degradation of lateral stiffness. Shear force of shape steel reacted at the core area of concrete section and led to tension effect which accelerated the damage of concrete. At the same time, the damage of concrete diminished its shear capacity and resulted in the shear enlargement of shape steel. The interplay between concrete damage and shear force of shape steel ultimately made for the failures of transfer columns. With the increase of extension length, the lateral stiffness first increases and then decreases, but the stiffness degradation gets faster; With the increase of steel ratio, the lateral stiffness remains the same, but the degradation gets faster; With the increase of the axial compression ratio, the lateral stiffness increases, and the degradation is more significant. Using more stirrups can effectively restrain the development of cracks and increase the lateral stiffness at the yielding point. Also, a formula for calculating the yielding lateral stiffness is obtained by a regression analysis of the test data.
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