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- 검색키워드
- 저자 : Jiangpeng Shu (검색결과 3 건)
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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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( Jiang Peng ),( Xiao Yong Zhang ),( Xin Ya Xu ),( Fei He ),( Shu Hua Qi ) 한국미생물 · 생명공학회 2013 Journal of microbiology and biotechnology Vol.23 No.4
The diversity of culturable non-actinobacterial (NA) bacteria associated with four species of South China Sea gorgonians was investigated using culture-dependent methods followed by analysis of the bacterial 16S rDNA sequence. A total of 76 bacterial isolates were recovered and identified, which belonged to 21 species of 7 genera, and Bacillus was the most diverse genus. Fifty-one percent of the 76 isolates displayed antibacterial activities, and most of them belonged to the Bacillus genus. From the culture broth of gorgonian-associated Bacillus methylotrophicus SCSGAB0092 isolated from gorgonian Melitodes squamata, 11 antimicrobial lipopeptides including seven surfactins and four iturins were obtained. These results imply that Bacillus strains associated with gorgonians play roles in coral defense mechanisms through producing antimicrobial substances. This study, for the first time, compares the diversity of culturable NA bacterial communities among four species of South China Sea gorgonians and investigates the secondary metabolites of gorgonian-associated B. methylotrophicus SCSGAB0092.
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