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  • Hindawi Limited  (5)
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  • Hindawi Limited  (5)
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  • 1
    Online Resource
    Online Resource
    Discussion on the Network Literacy Education System of College Students under the Big Data Media Management Model
    Hindawi Limited ; 2022
    In:  Journal of Sensors Vol. 2022 ( 2022-8-23), p. 1-12
    Details
    In: Journal of Sensors, Hindawi Limited, Vol. 2022 ( 2022-8-23), p. 1-12
    Abstract: Quality is strengthened by one’s own efforts in continuous learning. Quality education is a requirement for enhancing comprehensive national power. In a sense, it is of great significance to carry out quality education for college students, improve national quality, cultivate innovative talents, and ensure the quality of national and innovative talents, which is a prerequisite for social development. Quality education is the requirement of education reform in higher vocational colleges. Since entering the 21st century, many higher education institutions have slowly changed from elite education to mass education and from professional education in the past to professional education and quality education at the same time. This paper focuses on the study of college students’ network quality education system under the big data media management mode, using SQL Server big data management system, Oracle big data analysis research, and related score evaluation method to make an overall analysis of the current situation of China’s college students’ network quality education, and on this basis, we propose to improve academic standards, improve teachers’ quality education ability, follow academic freedom and equality, and improve the suggestions of improving academic standards, improving teachers’ quality education ability, following academic freedom and equality, improving teacher appointment system, optimizing evaluation system of quality education results, innovating incentive mechanism of university teachers’ online quality education, and creating benign development system of university teachers’ online quality education are proposed.
    Type of Medium: Online Resource
    ISSN: 1687-7268 , 1687-725X
    URL: Article
    DOI: 10.1155/2022/1517051
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2397931-8
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Foreign Object Detection in Railway Images Based on an Efficient Two-Stage Convolutional Neural Network
    Hindawi Limited ; 2022
    In:  Computational Intelligence and Neuroscience Vol. 2022 ( 2022-8-28), p. 1-10
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2022 ( 2022-8-28), p. 1-10
    Abstract: Foreign object intrusion is one of the main causes of train accidents that threaten human life and public property. Thus, the real-time detection of foreign objects intruding on the railway is important to prevent the train from colliding with foreign objects. Currently, the detection of railway foreign objects is mainly performed manually, which is prone to negligence and inefficient. In this study, an efficient two-stage framework is proposed for foreign object detection in railway images. In the first stage, a lightweight railway image classification network is established to classify any input railway images into one of two classes: normal or intruded. To enable real-time and accurate classification, we propose an improved inverted residual unit by introducing two improvements to the original inverted residual unit. First, the selective kernel convolution is used to dynamically select kernel size and learn multiscale features from railway images. Second, we employ a lightweight attention mechanism, called the convolutional block attention module, to exploit both spatial and channel-wise relationships between feature maps. In the second stage of our framework, the intruded image is fed to the foreign object detection network to further detect the location and class of the objects in the image. Experimental results confirm that the performance of our classification network is comparable to the widely used baselines, and it obtains outperforming efficiency. Moreover, the performances of the second-stage object detection are satisfying.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    URL: Article
    DOI: 10.1155/2022/3749635
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2388208-6
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    Online Resource
  • 3
    Online Resource
    Online Resource
    On the Application of BIM Technology and Neural Network Algorithm in the Study of Energy-Saving Optimization of the Envelope Structure of Assembled Buildings in the Western Sichuan Plain
    Hindawi Limited ; 2022
    In:  Journal of Sensors Vol. 2022 ( 2022-8-31), p. 1-12
    Details
    In: Journal of Sensors, Hindawi Limited, Vol. 2022 ( 2022-8-31), p. 1-12
    Abstract: With the rapid development of the economy, carbon neutral and carbon peak are on the agenda in China, and the reform of intelligent construction and assembly building is in full swing in the Chinese construction industry. In order to further explore and study the relationship between the textural structural issues of the envelope structure and the overall energy consumption situation of the house, and further bridge the gap between the actual energy-saving design scheme and the actual application scheme, therefore, this paper takes a certain place as a research object to study the phenomenon of design assembly energy saving, based on dozens of assembly building enterprises researched, selected samples of envelope components studied, selected BIM technology for premodeling optimization, used neural network algorithm for analysis of influencing factors, and studied the application in the study of energy-saving optimization of assembly building envelope in the western Sichuan plain area, and used OpenStudio was used to build a model to simulate the heating and air conditioning load of the assembled building numerically and to fit the quantitative relationship between the variable factors and the building energy consumption. On this basis, the relationship between multifactor and single-factor variables is identified and evaluated using relevant variables means.
    Type of Medium: Online Resource
    ISSN: 1687-7268 , 1687-725X
    URL: Article
    DOI: 10.1155/2022/1653838
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2397931-8
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    Online Resource
  • 4
    Online Resource
    Online Resource
    MRI-Based Medical Image Recognition: Identification and Diagnosis of LDH
    Hindawi Limited ; 2022
    In:  Computational Intelligence and Neuroscience Vol. 2022 ( 2022-9-9), p. 1-9
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2022 ( 2022-9-9), p. 1-9
    Abstract: To realize the automatic symptom recognition and classification of MR images and improve the accuracy and efficiency of the diagnosis of lumbar intervertebral disc herniation (LDH), a method for lumbar intervertebral disc recognition and disease classification is proposed in this paper. The method mainly includes three steps: preprocessing, target segmentation, and symptom classification. Preprocessing is performed by noise reduction and interference removal methods for blurred images. The contour poles are used to determine the four points of the tail vertebra in order to reduce the wrong segmentation of the tail vertebra. A classification method based on five judgment indicators is proposed, which effectively improves the stability of disease diagnosis. The example verifies that the algorithm can accurately complete the target segmentation and the accuracy of symptom classification reaches the standard of professional doctors, which proves that the method has good robustness.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    URL: Article
    DOI: 10.1155/2022/5207178
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2388208-6
    Location Call Number Limitation Availability
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    Online Resource
  • 5
    Online Resource
    Online Resource
    Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net
    Hindawi Limited ; 2022
    In:  Computational Intelligence and Neuroscience Vol. 2022 ( 2022-9-14), p. 1-10
    Details
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2022 ( 2022-9-14), p. 1-10
    Abstract: Lumbar spine segmentation is important to help doctors diagnose lumbar disc herniation (LDH) and patients’ rehabilitation treatment. In order to accurately segment the lumbar spine, a lumbar spine image segmentation algorithm based on improved Attention U-Net is proposed. The algorithm is based on Attention U-Net, the attention module based on multilevel feature map fusion is adopted, two residual modules are introduced instead of the original convolution blocks. a hybrid loss function is used for prediction during the training process, and finally, the image superposition process is realized. In this experiment, we expanded 420 lumbar MRI images of 180 patients to 1000 images and trained them by different algorithms, respectively, and accuracy, recall, and Dice similarity coefficient metrics were used to analyze these algorithms. The results show that compared with SVM, FCN, R-CNN, U-Net, and Attention U-Net models, the improved model achieved better results in all three evaluations, with 95.50%, 94.53%, and 95.01%, respectively, which proves the better performance of the proposed method for segmentation in lumbar disc and caudal vertebrae.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    URL: Article
    DOI: 10.1155/2022/4259471
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2388208-6
    Location Call Number Limitation Availability
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    Online Resource
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