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  • Economics  (3)
  • 1
    Online Resource
    Online Resource
    Improving QGA-ELM Inversion Model of Rice Leaf Area Index Based on UAV Remote Sensing Image
    Hindawi Limited ; 2022
    In:  Mobile Information Systems Vol. 2022 ( 2022-3-30), p. 1-15
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2022 ( 2022-3-30), p. 1-15
    Abstract: The leaf area index (LAI) is an important physiological parameter that characterizes the growth of crops. Traditional measurement could not meet the demands of large-scale accurate monitoring. QGA-ELM and LS-SVM algorithm combined with UAV remote sensing images was used to achieve the goal of building large-scale fast inversion modeling of LAI in this paper. Linear and nonlinear models were constructed for comparing the correlation between six spectral indices and LAI by categorizing the nitrogen level. The LS-SVM model was constructed to replace traditional linear model, the determination coefficient of correction set and prediction set (R2C and R2P) were 0.6496 and 0.6814; and the root mean square error of correction set and prediction set (RMSEC and RMSEP) were 0.5702 and 0.6842, respectively. The results showed that the inversion of edge objects in noncrop areas was not so stable. In order to address the problem, an improvement based on the extreme learning machine (ELM) and quantum genetic algorithm (QGA) with probabilistic evolution were used to combine with LS-SVM for overcoming the problem which the hidden layer connection weight and threshold randomly generated and solve the problems of slow regression of nonlinear data and insufficient model generalization ability. Compared with traditional linear and nonlinear regression, the QGA-ELM combined with LS-SVM showed the following: (1) improving the optimization ability greatly and avoid the prematurity of GA (genetic algorithm) effectively. The generalization performance has also been enhanced. (2) R2P of prediction set was 0.6686, and RMSEP was 0.8952 which could reflect the growth and distribution trend of rice in the regional scale. (3) Adapting different fertilization gradients (deficiency to excess) could provide basis for LAI inversion in different varieties and accumulated temperature zone of rice. The results above showed that QGA-ELM combined with LS-SVM could improve the stability of the model greatly and provide reference significance for rice growth inversion.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2022/9658966
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2187808-0
    Location Call Number Limitation Availability
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    Online Resource
  • 2
    Online Resource
    Online Resource
    GCP-Net: A Gating Context-Aware Pooling Network for Cervical Cell Nuclei Segmentation
    Hindawi Limited ; 2022
    In:  Mobile Information Systems Vol. 2022 ( 2022-4-9), p. 1-14
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2022 ( 2022-4-9), p. 1-14
    Abstract: Accurate segmentation of cervical nuclei is an essential step in the early diagnosis of cervical cancer. Still, there are few studies on the segmentation of clustered nuclei in clusters of cells. Because of the complexities of high cell overlap, blurred nuclei boundaries, and clustered cells, the accurate segmentation of clustered nuclei remains a pressing challenge. In this paper, we purposefully propose a GCP-Net deep learning network to handle the challenging cervical cluster cell images. The proposed U-Net-based GCP-Net consists of a pretrained ResNet-34 model as encoder, a Gating Context-aware Pooling (GCP) module, and a modified decoder. The GCP module is the primary building block of the network to improve the quality of feature learning. It allows the GCP-Net to refine details of feature maps leveraging multiscale context gating and Global Context Attention for the spatial and texture dependencies. The decoder block including Global Context Attention- (GCA-) Residual Block helps build long-range dependencies and global context interaction in the decoder to refine the predicted masks. We conducted extensive comparative experiments with seven existing models on our ClusteredCell dataset and three typical medical image datasets, respectively. The experimental results showed that the GCP-Net obtained promising results on three evaluation metrics AJI, Dice, and PQ, demonstrating the superiorities and generalizability of our GCP-Net for automatic medical image segmentation in comparison with some SOAT baselines.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2022/7511905
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2187808-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Feature Extraction Method of Snowboard Starting Action Using Vision Sensor Image Processing
    Hindawi Limited ; 2022
    In:  Mobile Information Systems Vol. 2022 ( 2022-1-19), p. 1-9
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2022 ( 2022-1-19), p. 1-9
    Abstract: There is a lot of noise in the snowboard starting action image, which leads to the low accuracy of snowboard starting action feature extraction. We propose a snowboard starting action feature extraction using visual sensor image processing. Firstly, the overlapping images are separated by laser fringe technology. After separation, the middle point of the image is taken as the feature point, and the interference factors are filtered by laser. Secondly, the three-dimensional model is established by using visual sensing image technology, the action feature images are input in the order of recognition, and all actions are reconstructed and assembled to complete the action feature extraction of snowboard. The interference factors are filtered by laser, the middle part of the action image is extracted according to the common features of multiple images, and its definition is described. The movement change and moving distance are used to count the most features and clarity. Finally, the edge recognition effect of snowboard starting action image and the action recognition effect under multiple complex images are taken as experimental indexes. The results show that the method has a good effect on image edge extraction, the extraction effect is as high as 95%, and the accuracy is as high as 2.1%. In addition, under multiple complex images, the action feature recognition rate is also high, which can prove that the method studied has better accuracy in snowboard starting action feature extraction.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2022/2829547
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2187808-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
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