Local stability of concrete arch bridge based on Ritz method

In order to minimize the self-weight and prevent local buckling failure of thin-walled box concrete arch bridges at the same time, the limit values of width-thickness ratios are deduced based on Ritz method and equivalent strut theory of arch bridge. A new method of determining sectional forms based on the limit values of width-thickness ratios is put forward. Based on Mupeng bridge, the theoretical results are verified by finite element software ANSYS. Results show that the limits of width-thickness ratios are related to concrete grade, equivalent calculation length and radius of gyration, the allowable minimum thickness of Mupeng bridge is 15 cm to avoid local buckling. The limit values of width-thickness ratios deduced in this paper are reasonable and this new method of determining sectional forms is simple and rational to apply in engineering. A scientific engineering calculation method on arch ring design is put forward and it can provide a theoretical basis for the design of thin-walled box concrete arch bridges constructed by cantilever pouring.

New progress in international nanotechnology research in the past ten years – visual analysis based on CitesSpace

With the continuous development of high-tech industry, Moore’s law is close to the limit. People urgently need nano science and technology to trigger a new scientific and technological revolution to meet the needs of life, military and so on. Nanotechnology covers almost all industries and has made achievements in the industries such as medical, materials, manufacturing, and information technology. It has changed the production and life of human beings and subverted many industries. In recent years, more and more people have conducted data mining on nanotechnology research. By combing the literature, this paper summarizes the core authors, keyword changes, important authors and emergent words of the existing literature. Contributing to analyzing the research status of this field and revealing research hotspots in this field. It is of great significance for scholars to sort out the development process of nano field and predict the future development trend. Using CitesSpace bibliometric analysis software, 44002 pieces of literature about nanotechnology in SCI and SSCI journals in the core collection of the Web of Science database were analyzed in this paper. The results indicated that countries such as the United States, Germany, China, and Japan have issued more articles; However, the centrality of articles published in European countries such as the UK, Germany, and France was relatively strong; High-yield units mainly included Chinese Acad Sci and Russian Acad Sci; The main research scholars were Wei Wang, Peixuan Guo, Thomas J Webster, Hao Yan; Research emergent words primarily included polymer, particle, dynamics, mechanical properties and silver nanoparticle. On this basis, countermeasure suggestions and prospects are proposed.

Design of diversified teaching platform of college aerobics course based on artificial intelligence

In order to improve the diversified teaching effect of a college aerobics course, effectively improve the accuracy of student grouping on the teaching platform, a diversified teaching platform of college aerobics course based on artificial intelligence is designed. First of all, it puts forward the construction idea and design process of the network teaching platform, then designs the interface and function module of the teaching platform, and finally designs the grouping function of teaching objects, so as to complete the design of the diversified teaching platform of a college aerobics course based on artificial intelligence. The experimental results show that the grouping accuracy of students on the diversified teaching platform of college aerobics course based on artificial intelligence is greater than 75%, and the average score of students studying on the platform is 74.66. This explains why the designed platform can effectively provide the accuracy of grouping and the students’ performance.

Semi supervised inspection algorithm of automatic packaging curve based on deep learning

The current automatic packaging process is complex, requires high professional knowledge, poor universality, and difficult to apply in multi-objective and complex background. In view of this problem, automatic packaging optimization algorithm has been widely paid attention to. However, the traditional automatic packaging detection accuracy is low, the practicability is poor. Therefore, a semi-supervised detection method of automatic packaging curve based on deep learning and semi-supervised learning is proposed. Deep learning is used to extract features and posterior probability to classify unlabeled data. KDD CUP99 data set was used to verify the accuracy of the algorithm. Experimental results show that this method can effectively improve the performance of automatic packaging curve semi-supervised detection system.

Tobit model-based analysis on the influencing factors of wheat loss during harvesting by the combine harvester

Based on the investigation data of wheat mechanized harvest in eight major wheat producing areas from the south to the north of Henan Province, the main factors affecting wheat mechanized harvest loss were identified and the influence of each factor was decomposed. In this article, the loss rate of wheat mechanical harvest was calculated by using the method of artificial measurement of wheat yield in the field, and the influencing factors of wheat mechanical harvest operation in 8 regions of Henan province were treated and analyzed by using Tobit regression model. In this paper, the loss rate of wheat mechanical harvest was calculated by using the method of wheat field artificial yield measurement and the influencing factors of wheat mechanical harvest operation in eight regions of Henan province were treated and analyzed by using Tobit regression model. The results show that the average harvest loss rate in the field amounts to 2.96%, the average harvest loss rate at the edge of field amounts to 3.06%, whereas the loss rate in the normal operation area amounts 2.86%. The main factors that caused the harvest loss of wheat field machinery were the maturity of wheat, the area of operation field, the diseases and pests, weather conditions and the accumulated working hours of harvester drivers in a single day. Therefore, the main technical measures to reduce the operation loss of wheat combine harvester were put forward to provide a theoretical basis for promoting the deep integration of agricultural machinery and agronomy.

The method of repairing OHE damaged ancient painted murals based on machine vision

Traditional mural repair methods only observe the texture of murals when segmenting the repair area, but ignore the extraction of a mural damage data, resulting in incomplete damage crack information. For this reason, the method of repairing the damaged murals based on machine vision is studied. Using machine vision, it can get two-dimensional image of a mural, preprocess the image, extract the damaged data of a mural, and then divide the repair area and repair degree index. According to different types of damage, it can choose the corresponding repair methods to achieve the repair of damaged mural. The results show: Compared with the reference [1] method and reference [2] method, the number of repair points and repair cracks extracted by the proposed method is more than that of the two traditional methods, which can more accurately and comprehensively extract the repair information of murals.

Automatic verification method of progressive image model matching information based on machine learning

In order to solve the problems of large error and low performance of traditional progressive image model matching information checking methods, an automatic progressive image model matching information checking method based on machine learning is proposed. The generation method of progressive image is analyzed, and the target image sample is obtained. On this basis, machine learning algorithm is used to segment progressive image samples. In each image segmentation part, crawler technology is used to automatically collect progressive image model matching information, and under the constraint of image model matching information checking standard, automatic checking of progressive image model matching information is realized from geometric structure, image content and other aspects. Experimental results show that the verification error of the design method is reduced by 0.687 Mb, and the quality of progressive image is improved.

Neural network-based optimization model for sprint data collection

Sprint data has the characteristics of quality and continuity, but due to the limitations of optimization algorithm, the existing sprint data acquisition optimization model has the problem of low optimization performance parameters. Therefore, a data acquisition control optimization model based on neural network is proposed. This paper analyzes the advantages and disadvantages of neural network algorithm, combined with the sprint data collection optimization requirements, introduces BP neural network algorithm, based on this, uses multiple sensors, based on baud interval balance control to collect sprint data, applies BP neural network algorithm to compress, integrate and classify sprint data, realizes the sprint data collection and optimization. The experimental results show that the optimization performance parameters of the model are large, which fully shows that the model has good data acquisition optimization performance.

Image recognition based on Boltzmann machine algorithm

Due to the influence of recognition parameters, image recognition has low recognition accuracy, long recognition time and large storage cost. Therefore, an automatic image recognition method based on Boltzmann machine is proposed. Based on threshold method and fuzzy set method, image malformation correction is performed. The mean filter and median filter are combined to eliminate the influence of image filtering, and the pre-processing of image is completed by using the fuzzy enhancement of image. Based on the restricted Boltzmann method, the network model is dynamically evolved, and the identification parameters of each shape and contour are obtained. Different shapes and contours are classified and recognized. Simulation results show that image recognition method based on human-computer interaction has high recognition ability, shortens the time cost and greatly reduces the space needed for node storage.

Virtual interaction algorithm of cultural heritage based on multi feature fusion

During the traditional cultural heritage virtual interaction algorithm in the interaction action recognition, the database is too single, resulting in low recognition accuracy, recognition time-consumer and other issues. Therefore, this paper introduces the multi feature fusion method to optimize the cultural heritage virtual interaction algorithm. Kinect bone tracking technology is applied to identify the movement of the tracking object, 20 joints of the human body are tracked, and interactive action recognition is realized according to the fingertip candidate points. In order to carry out the judgment virtual interactive operation of subsequent recognition actions, a multi feature fusion database is established. The mean shift is used to derive the moving mean of the target’s action position and to track the interactive object. The Euclidean distance formula is used to train samples of multi feature fusion database data to realize the judgment of recognition action and virtual interaction. In order to verify the feasibility of the research algorithm, the virtual interactive script of ink painting in a cultural heritage museum is used to simulate the research algorithm, and a comparative experiment is designed. The experimental results show that the proposed algorithm is superior to the traditional virtual interactive algorithm in recognition accuracy and efficiency, which proves the feasibility of this method.