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Ancient buildings have strict standards for vibration control. Effectively identifying vibration types and controlling construction vibrations during construction activities is advantageous to the structural safety of ancient buildings. This study is based on an analysis of vibration data from the top, foundation, and bedrock of the White Pagoda in Hangzhou, Zhejiang province, which is an ancient building. Considering the surrounding construction and wind environment, this study focuses on analyzing the data features of tower vibrations under three types of structural vibration. We propose a support vector machine (SVM) vibration identification method that incorporates multi-feature parameters and multi-sensor signal correlation. This method effectively identifies the source of structural vibration by distinguishing between typical wind-induced vibrations, typical construction vibrations, and typical mixed vibrations. The application of this method could guide construction activities and mitigate the safety impacts of construction and mixed vibrations on historical building structures.

Underwater bubble plume images contain a wealth of information on wave field and flow characteristics, which can provide valuable research data for marine development, environmental protection, and underwater surveys. However, based on fusing image features and wave field environment features, identifying accurately the underwater bubble plume is still very difficult. In order to improve the accuracy and robustness of bubble plume identification in complex underwater environments, an underwater bubble plume recognition algorithm based on multi-feature fusion understanding is proposed. In this paper, a weight-independent dual-channel residual convolutional neural network (CNN) for feature extraction of the original optical images and the nonsubsampled contourlet transform (NSCT) low-frequency images, and the multi-scale composite feature map groups are generated. Then adaptive fusion is performed based on the feature contribution of the target in different types of images. Next, logical region of interest (ROI) masks are generated by the attention mechanism and superimposed on the fused image to further highlight the target features. Finally, the multi-scale dual-channel fused feature maps containing ROI masks are used for underwater bubble plume target recognition. The experimental results show that the designed recognition network can effectively fuse the features of the original optical images and the NSCT low-frequency imagers, improve the depth of information fusion, and retain the target texture features and the morphological features while reducing the interference of the background information, and have good recognition accuracy and robustness for multi-scale bubble targets in the underwater environment.

With the rapid growth of the rural tourism industry, traditional tourism recommendation technologies can no longer meet the necessary requirements. To address the issue of rural tourist attraction recommendations, a rural tourist attraction recommendation model is constructed based on a multi-feature fusion graph neural network. First, construct a feature map based on the relationship between tourists’ preferences and tourist attractions, and incorporate the attention mechanism to enhance the model’s learning capabilities. Second, utilize a two-part graph model to extract positive and negative preference features of tourists, and a conversation graph model to extract tourists’ transfer preference features. Finally, various features are utilized to generate suggested content by computing scores for tourists’ travel preferences. To address the problem of recommending tourist groups, suitable features for random group matching are collected and the cosine function is employed to identify users with similar random group features. Finally, the multi-features are merged, and the tourists’ interest preferences are scored to arrive at content recommendations. In the experiment on individualized attraction recommendations, data from the Chengdu area were used to test the proposed model. The accuracy of the model’s recommendations was 0.822 for five recommendations which outperformed the other models. In the experiment for group-based attraction recommendations, this experiment tested the Chengdu dataset. The proposed model achieved the highest accuracy of 0.972 when the group size was 70, outperforming the other two models. Additionally, with regards to different numbers of recommendations, the proposed model’s accuracy was 0.5241, which was the best performance among the three models when the number of recommendations was set to five. The proposed recommendation model performs optimally in suggesting tourist attractions and meets the needs of rural tourism. The research content provides crucial technical references for tourist traveling and rural tourism development.

A new segmentation algorithm, which utilizes boundary, area and texture features, for segmenting extended target in complex environment is proposed in this paper. First, according to the characteristics of extended target, the target is preliminary segmented by series boundary technique based on knowledge. Then a new fractal segmentation algorithm is proposed to remove the complex natural background blocks. Finally, the mathematical morphology method is utilized to eliminate the background conglutination. The experimental result indicated the validity of our method for segmenting the extended target in complex environment and the method can reserve the shape details of the target perfectly.