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  • 1
    Online Resource
    Online Resource
    IOP Publishing ; 2021
    In:  IOP Conference Series: Earth and Environmental Science Vol. 643, No. 1 ( 2021-01-01), p. 012026-
    In: IOP Conference Series: Earth and Environmental Science, IOP Publishing, Vol. 643, No. 1 ( 2021-01-01), p. 012026-
    Abstract: In order to study the influence of stirrup corrosion level on the peak stress, peak strain, ultimate strain and shape of stress-strain curve of the confined concrete, 15 reinforced concrete prism specimens were subjected to acid rain erosion by artificial climate simulation technique followed by axial pressure tests. Based on Mander’s model and the existing research results, the calculation formulas of the peak stress f’ cco , peak strain £ cco , ultimate strain ε cco , ultimate strain ε cu0 and shape factor r of the uncorroded specimens are determined. The factor calculation formulas for peak stress, peak strain, ultimate strain and shape factor of corroded specimens is developed by regression analysis of test data, respectively, and then the constitutive model of confined concrete by acid rain erosion is established. By comparing the simulation results with the experimental data, it can be found that all the peak stress, peak strain, ultimate strain and stress-strain curves shape of the specimens obtained by the proposed method are in good agreement with the experimental data. Thus the constitutive mode for confined concrete established in this paper can accurately reflect the mechanical performance of RC prism specimen by acid rain erosion, indicating its adaptiveness for estimating the residual bearing capacity and the seismic performance of RC structure under the acid rain environment.
    Type of Medium: Online Resource
    ISSN: 1755-1307 , 1755-1315
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 2434538-6
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  • 2
    In: Frontiers in Medicine, Frontiers Media SA, Vol. 10 ( 2023-7-25)
    Abstract: With the development of arthroscopic technology and equipment, arthroscopy can effectively repair the tear of the subscapular muscle. However, it is difficult to expose the subscapular muscle and operate it under a microscope. In this study, the SwiveLock ® C external row anchor under arthroscopy was applied to repair the tear of the subscapular muscle in a single row, which is relatively easy to operate with reliable suture and fixation, and its efficacy was evaluated. Purpose This study aimed to assess the clinical efficacy and the tendon integrity of patients who had subscapularis tears by adopting the single-row repair technique with a SwiveLock ® C external row anchor. Methods Patients who had the subscapular muscle tear either with or without retraction were included, and their follow-up time was at least 1 year. The degree of tendon injury was examined by magnetic resonance imaging (MRI) and confirmed by arthroscopy. The tendon was repaired in an arthroscopic manner by utilizing the single-row technique at the medial margin of the lesser tuberosity. One double-loaded suture SwiveLock ® C anchor was applied to achieve a strong fixation between the footprint and tendon. The range of motion, pain visual simulation score, American Shoulder and Elbow Surgeons (ASES) score, and Constant score of shoulder joint were evaluated for each patient before the operation, 3 months after the operation, and at least 1 year after the operation. Results In total, 110 patients, including 31 males and 79 females, with an average age of 68.28 ± 8.73 years were included. Arthroscopic repair of the subscapular tendon with SwiveLock ® C external anchor can effectively improve the range of motion of the shoulder joint. At the last follow-up, the forward flexion of the shoulder joint increased from 88.97 ± 26.33° to 138.38 ± 26.48° ( P & lt; 0.05), the abduction range increased from 88.86 ± 25.27° to 137.78 ± 25.64° ( P & lt; 0.05), the external rotation range increased from 46.37 ± 14.48° to 66.49 ± 14.15° ( P & lt; 0.05), and the internal rotation range increased from 40.03 ± 9.01° to 57.55 ± 7.43° ( P & lt; 0.05). The clinical effect is obvious. The constant shoulder joint score increased from 40.14 ± 15.07 to 81.75 ± 11.00 ( P & lt; 0.05), the ASES score increased from 37.88 ± 13.24 to 82.01 ± 9.65 ( P & lt; 0.05), and the visual analog scale score decreased from 5.05 ± 2.11 to 1.01 ± 0.85 ( P & lt; 0.05). In the 6th month after the operation, two cases (1.81%) were confirmed to have re-tears via MRI. Conclusion In this study, we repaired the subscapularis muscle with a single-row technique fixed by SwiveLock ® C anchor and FiberWire ® sutures and evaluated its efficacy. The results showed that the clinical effect of single-row arthroscopic repair was satisfactory and that reliable tendon healing could be achieved.
    Type of Medium: Online Resource
    ISSN: 2296-858X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2775999-4
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  • 3
    Online Resource
    Online Resource
    IOP Publishing ; 2021
    In:  IOP Conference Series: Earth and Environmental Science Vol. 643, No. 1 ( 2021-01-01), p. 012076-
    In: IOP Conference Series: Earth and Environmental Science, IOP Publishing, Vol. 643, No. 1 ( 2021-01-01), p. 012076-
    Abstract: In alkaline and near-neutral soil environments, the mechanical and seismic performance of buried steel pipes degrades with deepening corrosion over time. To study the seismic fragility of buried steel pipes of different service ages in these environments, an incremental dynamic time history analysis of typical pipes was carried out with the time-varying constitutive model of steel. A probabilistic seismic demand model for buried steel pipes of different service ages in alkaline and near-neutral soil environments was then established, which can characterize the probability relationship between the ground motion intensity and structural response. Furthermore, on the basis of the tristate criteria, the limits of each ultimate failure state were determined. Time-varying seismic analytical fragility models of the pipes, including pipe units in two different soil environments and four service ages, were then established, which can characterize the probability of different failure states of structures under different earthquakes. The corresponding seismic fragility curves were then drawn. Seismic fragility curves were also obtained under three different diameter ranges based on seismic damage statistics. Results showed that, under the same ground motion, with increasing service time and decreasing pipe diameter, the probabilities of three failure states, namely, basically intact, moderately damaged, and severely damaged, all increased.
    Type of Medium: Online Resource
    ISSN: 1755-1307 , 1755-1315
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 2434538-6
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  • 4
    In: Nature Medicine, Springer Science and Business Media LLC, Vol. 29, No. 2 ( 2023-02), p. 493-503
    Type of Medium: Online Resource
    ISSN: 1078-8956 , 1546-170X
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 1484517-9
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  • 5
    Online Resource
    Online Resource
    MDPI AG ; 2023
    In:  Buildings Vol. 13, No. 5 ( 2023-05-11), p. 1258-
    In: Buildings, MDPI AG, Vol. 13, No. 5 ( 2023-05-11), p. 1258-
    Abstract: Seismic damage assessment of reinforced concrete (RC) structures is a vital issue for post-earthquake evaluation. Conventional onsite inspection depends greatly on subjective judgments and engineering experiences of human inspectors, and the efficiency is limited to large-scale urban areas. This study proposes a computer-vision and machine-learning-based seismic damage assessment framework for RC structures. A refined Park-Ang model is built to express the coupled effects of structural ductility and energy dissipation, which reflects the nonlinear seismic damage accumulation and generates a synthetical seismic damage indicator within 0~1 using hysteretic curve data. A deep neural network is established to regress the damage indicator using damage-related and design-related parameters as inputs. The results show that the correlation coefficients between the predicted and actual seismic damage index exceed 0.98, and the predicted seismic damage index is unbiased and stable without overfitting. Furthermore, the effectiveness, robustness, and generalization ability of the proposed method are verified.
    Type of Medium: Online Resource
    ISSN: 2075-5309
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2661539-3
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  • 6
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2022
    In:  Neural Computing and Applications Vol. 34, No. 5 ( 2022-03), p. 3385-3398
    In: Neural Computing and Applications, Springer Science and Business Media LLC, Vol. 34, No. 5 ( 2022-03), p. 3385-3398
    Abstract: Although convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.
    Type of Medium: Online Resource
    ISSN: 0941-0643 , 1433-3058
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 1136944-9
    detail.hit.zdb_id: 1480526-1
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  • 7
    In: Journal of Food Process Engineering, Wiley, Vol. 44, No. 2 ( 2021-02)
    Abstract: As an important link in the processing of jujube products, the qualities classification of jujubes have an important impact on improving the value of commodities. In this study, jujube target was extracted based on the RGB color space characteristics and then put into a black background through a mask. The data augmentation method combined deep convolutional generative adversarial networks and rigid transformation (RT) was used to improve the data richness of defective jujubes, effectively solve the imbalance problem between different types of jujube data. A composite convolutional neural network (CNN) method based on residual networks was designed to effectively solve the problem of misjudgment between jujubes with subtle defects and healthy jujubes. The overall results illustrated that the defect detection accuracy of the proposed scheme was 99.2%, which was superior to the widely used support vector machine and CNN methods. This work could be applied to the actual processing site and greatly improved the quality classification effect of jujubes. Practical Applications Cracks, peeling, wrinkles, and other defects have seriously affected the quality and value of jujubes, and the quality classification of jujubes is imperative. This paper proposes a set of deep learning schemes from three aspects of improving data quality, enhancing data richness, and designing more accurate and effective classification models. Experimental results show that this scheme can significantly improve the accuracy of jujube quality grading.
    Type of Medium: Online Resource
    ISSN: 0145-8876 , 1745-4530
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2175259-X
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  • 8
    In: Sensors, MDPI AG, Vol. 20, No. 21 ( 2020-11-05), p. 6305-
    Abstract: With the rapid development of information technology and the widespread use of the Internet, QR codes are widely used in all walks of life and have a profound impact on people’s work and life. However, the QR code itself is likely to be printed and forged, which will cause serious economic losses and criminal offenses. Therefore, it is of great significance to identify the printer source of QR code. A method of printer source identification for scanned QR Code image blocks based on convolutional neural network (PSINet) is proposed, which innovatively introduces a bottleneck residual block (BRB). We give a detailed theoretical discussion and experimental analysis of PSINet in terms of network input, the first convolution layer design based on residual structure, and the overall architecture of the proposed convolution neural network (CNN). Experimental results show that the proposed PSINet in this paper can obtain extremely excellent printer source identification performance, the accuracy of printer source identification of QR code on eight printers can reach 99.82%, which is not only better than LeNet and AlexNet widely used in the field of digital image forensics, but also exceeds state-of-the-art deep learning methods in the field of printer source identification.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2052857-7
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  • 9
    In: Chinese Physics B, IOP Publishing, Vol. 25, No. 7 ( 2016-07), p. 076105-
    Type of Medium: Online Resource
    ISSN: 1674-1056
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2016
    detail.hit.zdb_id: 2412147-2
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  • 10
    Online Resource
    Online Resource
    MDPI AG ; 2019
    In:  Applied Sciences Vol. 9, No. 7 ( 2019-03-31), p. 1364-
    In: Applied Sciences, MDPI AG, Vol. 9, No. 7 ( 2019-03-31), p. 1364-
    Abstract: Roller bearings are some of the most critical and widely used components in rotating machinery. Appearance defect inspection plays a key role in bearing quality control. However, in real industries, bearing defects are usually extremely subtle and have a low probability of occurrence. This leads to distribution discrepancies between the number of positive and negative samples, which makes intelligent data-driven inspection methods difficult to develop and deploy. This paper presents a small data-driven convolution neural network (SDD-CNN) for roller subtle defect inspection via an ensemble method for small data preprocessing. First, label dilation (LD) is applied to solve the problem of an imbalance in class distribution. Second, a semi-supervised data augmentation (SSDA) method is proposed to extend the dataset in a more efficient and controlled way. In this method, a coarse CNN model is trained to generate ground truth class activation and guide the random cropping of images. Third, four variants of the CNN model, namely, SqueezeNet v1.1, Inception v3, VGG-16, and ResNet-18, are introduced and employed to inspect and classify the surface defects of rollers. Finally, a rich set of experiments and assessments is conducted, indicating that these SDD-CNN models, particularly the SDD-Inception v3 model, perform exceedingly well in the roller defect classification task with a top-1 accuracy reaching 99.56%. In addition, the convergence time and classification accuracy for an SDD-CNN model achieve significant improvement compared to that for the original CNN. Overall, using an SDD-CNN architecture, this paper provides a clear path toward a higher precision and efficiency for roller defect inspection in smart manufacturing.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2704225-X
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