In view of the operational efficiency,safety,service and development of the metro system and other application research needs,a multi-level and multi-resolution simulation model system for subway lines is construc-ted,and the application of modeling methods based on building information model(BIM),cloud data and mathemat-ical analysis,geographic information system(GIS)and hybrid simulation are analyzed.This paper discusses the key problems and measures such as multi-resolution modeling,resolution switching rules for space-time coupling and model aggregation algorithm based on dynamic weight that need to be solved in the application of model system.Ac-cording to the industry case study,the effectiveness and feasibility of the methods proposed in this paper in practical applications are verified.Finally,a summary and outlook are made.The research results have important theoretical significance and practical value for metro line planning,operation management,and development.
Breast cancer is the most dangerous killer for women. Accurate early diagnosis is the first step of treatment. This paper proposes a breast cancer detection model CBAMDNet based on breast pathological sections and deep learning. We adopt a pre-trained DenseNet121 embedded with spatial attention mechanism as the backbone model. Three random network models are trained in CBAMDNet to predict, and fused through majority voting to obtain more accurate results. We used a breast pathological section image data set to evaluate the generalization ability of our CBAMDNet, using 4 times cross validation. Simulation experiments show that CBAMDNet can produce higher classification results than the four existing breast cancer classification methods. Therefore, our CBAMDNet is an accurate tool to detect breast cancer and can be used for clinical diagnosis.
It is one of the key directions of the new infrastructure to establish an experimental teaching platform for synergy between science and education, thus promoting the construction of intelligent laboratories. As a new engineering major supporting the development of national strategic emerging industries, the experimental teaching of Internet of Things (IoT) engineering is crucial in the process of talent training. However, the existing experimental teaching system is constrained by many factors including funding, location, faculty strength, and network conditions, which make it difficult to implement large-scale and creative IoT experiments, especially process-integrated experiments. This paper focuses on how to build an experimental teaching process and experimental teaching system that meets the needs of the IoT industry and talent training under the new infrastructure perspective. By analysing the IoT curriculum structure and talent demand, the deficiencies of the existing experimental teaching platform are discussed in combination with the school's talent training positioning and the current IoT industry chain situation. Simultaneously, the technical system structure of IoT is set as the main theme to sort out the knowledge points of IoT experimental teaching, and then a virtual simulation platform of IoT experimental teaching is proposed. The practical application on National Cyberspace proves that the proposed experimental platform can meet the needs of basic experiments, engineering practical training, innovation, scientific research assistance, and industrial applications.
This study aims at applying the improved You Only Look Once V5s model for the assessment of regional poverty using remote sens- ing image target detection. The model was improved from structure, algorithm, and components. Objects in the remote sensing images were used to identify poverty, and the poverty alleviation situation could be predicted according to the existing detection results. The results showed that the values of Precision, Recall, mean Average Precision (mAP)@0.5, and mAP@0.5:0.95 of the model increased 7.3%, 0.7%, 1%, and 7.2%, respectively on the Common Objects in Context data set in the detection stage; the four values increased 3.1%, 2.2%, 1.3%, and 5.7%, respectively on the custom remote sensing image data set in the verification stage. The loss values decreased 2.6% and 37.4%, respectively, on the two data sets. Hence, the ap- plication of the improved model led to the more accurate detection of the targets. Compared with the other papers, the improved model in this paper proved to be better. Artificial poverty alleviation can be replaced by remote sensing image processing because it is inex- pensive, efficient, accurate, objective, does not require data, and has the same evaluation effect. The proposed model can be considered as a promising approach in the assessment of regional poverty.
Due to unreasonable distribution of urban vertical green space,this paper puts forward a method for ex-tracting vertical distribution features of high-density urban greening system.Firstly,an acquisition platform of remote sensing images based on UAV was used to collect greening system images.Secondly,a convolutional neural network architecture was constructed,and then the noise was removed from the initial image through residual learning.The tas-seled hat transformation algorithm was adopted to rotate the image axis,thus enhancing the image,and highlighting the details.According to the similarity and discontinuity of the spectrum,a data-driven algorithm was adopted to deter-mine a homogeneity standard.After that,the image was divided into different areas to ensure that the same target was in the same area.Finally,the vertical distribution features of the greening system were extracted from three aspects:spectrum,shape and texture.Experimental results prove that the vertical distribution feature of the commercial area is similar with that of the residential area,but the green area of the residential area is larger,and the plant species in commercial area are more abundant.In addition,the extracted features can provide reasonable plan.
