Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description

In view of the issue that the features of the images in the shallow layer cannot be fully utilized when the image description is generated and the target association of the image cannot be sufficiently obtained, a generation method for the description of the acquisition of attention images is put forward in this paper. The proportions of the features of images at various depths are autonomously assigned based on the content data of the language model, and the images thus generated are all pictures with image features with attention. In this way, the effect of description generation of images has been improved. After the testing of the database, the results indicate that the calculation method of the algorithm put forward in this paper is more accurate than the top-down multimedia image algorithm generated by a single attention.

Internet Of Things (IoT) In Water Quality Monitoring Systems And Nutrition In Hydroponic Plants

Population is growing every year. This has an impact on the reduction of agricultural land to cultivate crops. This study aims to combine a concept that aims to expand the benefits of continuously connected internet connectivity. Based on the long term, the narrowing of agricultural land will have an impact on the scarcity of hydroponic NFT (Nutrient Film Technique) is a model of cultivation by putting the roots of plants in a shallow layer of water. The water is circulated and contains nutrients according to the needs of plants. This study combines hydroponic plants with the help of Internet of Things (IoT) technology using hydroponic planting techniques. Rooting can develop in a nutrient solution, because around rooting there is a layer of nutrient solution then the system is known as NFT. Excess water reduces the amount of oxygen and dissolved nutrients. The use of a manual measuring instrument is actually time-consuming if the owner is busy. Based on this background, the author got the idea to create a system of monitoring water quality and nutrients in hydroponic plants that can be accessed through a mobile phone.

Ancient mural classification methods based on a multichannel separable network

Ancient murals are of high artistic value and boast rich content. The accurate classification of murals is a challenging task for researchers and can be arduous even for experienced researchers. The image classification algorithms currently available are not effective in the classification of mural images with strong background noise. A new multichannel separable network model (MCSN) is proposed in this study to solve this issue. Using the GoogLeNet network model as the basic framework, we adopt a small convolution kernel for the extraction of the shallow-layer background features of murals and then decompose larger, two-dimensional convolution kernels into smaller convolution kernels, for example, 7 × 7 and 3 × 3 kernels into 7 × 1 and 1 × 7 kernels and 3 × 1 and 1 × 3 kernels, respectively, to extract important deep-layer feature information. A soft thresholding activation scaling strategy is adopted to enhance the stability of the network during training, and finally, the murals are classified through the softmax layer. A minibatch SGD algorithm is employed to update the parameters. The accuracy, recall and F1-score reached 88.16%, 90.01%, and 90.38%, respectively. Compared with mainstream classification algorithms, the model demonstrates improvement in terms of classification accuracy, generalizability, and stability to a certain extent, supporting its suitability in efficiently classifying murals.

MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation

Segmentation of retinal vessels is important for doctors to diagnose some diseases. The segmentation accuracy of retinal vessels can be effectively improved by using deep learning methods. However, most of the existing methods are incomplete for shallow feature extraction, and some superficial features are lost, resulting in blurred vessel boundaries and inaccurate segmentation of capillaries in the segmentation results. At the same time, the “layer-by-layer” information fusion between encoder and decoder makes the feature information extracted from the shallow layer of the network cannot be smoothly transferred to the deep layer of the network, resulting in noise in the segmentation features. In this paper, we propose the MFI-Net (Multi-resolution fusion input network) network model to alleviate the above problem to a certain extent. The multi-resolution input module in MFI-Net avoids the loss of coarse-grained feature information in the shallow layer by extracting local and global feature information in different resolutions. We have reconsidered the information fusion method between the encoder and the decoder, and used the information aggregation method to alleviate the information isolation between the shallow and deep layers of the network. MFI-Net is verified on three datasets, DRIVE, CHASE_DB1 and STARE. The experimental results show that our network is at a high level in several metrics, with F1 higher than U-Net by 2.42%, 2.46% and 1.61%, higher than R2U-Net by 1.47%, 2.22% and 0.08%, respectively. Finally, this paper proves the robustness of MFI-Net through experiments and discussions on the stability and generalization ability of MFI-Net.

Shallow Layer Destruction Law of Expansive Soil Slope under Rainfall and the Application of Geogrid Reinforcement

A large number of instability cases and laboratory tests of expansive soil slopes show that its shallow layer destruction happens because of the insufficient shear strength under the usual action of low stress and repeated dry-wet cycles. We can obtain the strength nonlinear distribution law fitted by generalized power function based on a series of shear strength tests of expansive soil considering low stress and can construct the numerical model considering the nonlinear strength distribution by FISH, to realize the shear strength dynamic distribution with the vertical stress. Based on the numerical model, the whole-process contrastive analysis has been conducted on the stress field, the slip surface depth, and the seepage field of plain soil and reinforced expansive soil cut slope under different rainfall conditions. Besides, the mechanics characteristic of the geogrid under various design schemes has been compared and analyzed. A further explanation has been given for the expansive soil cut slope prone to shallow layer failure after rainfall and on the effect of geogrid reinforcement. The numerical results provide a reference for slope stability analysis in rainy expansive soil areas.

The Impact of a Midlevel Dry Air Flow Layer on Deep Convection in the pre-Gabrielle (2013) Tropical Disturbance on 4–5 September

On 4–5 September 2013, a relatively shallow layer of northerly dry air flow was observed just west of the core deep convection associated with the low-level center of the pre-Gabrielle (2013) tropical disturbance. Shortly thereafter, the core deep convection of the disturbance collapsed after having persisted for well over 24 hours. The present study provides an in-depth analysis of the interaction between this dry air flow layer and the pre-Gabrielle disturbance core deep convection using a combination of observations, reanalysis fields, and idealized simulations. Based on the analysis, we conclude that the dry air flow layer played an important role in the collapse of the core deep convection in the pre-Gabrielle disturbance. Furthermore, we found that the presence of storm-relative flow was critical to the inhibitive effects of the dry air flow layer on deep convection. The mechanism by which the dry air flow layer inhibited deep convection was found to be enhanced dry air entrainment.