Abstract This paper proposes a shape context shape context algorithm combined with DTW dynamic regularization to obtain the data of the skeleton joint points of the dance posture, and calculate the similarity and key frame time of the joint points by DTW dynamic time regularization matching to complete the quantitative analysis of the dancer’s movement and rhythm. This paper is based on the application of national dance classes in colleges and universities. The corresponding research results can provide theoretical data reference for dance teachers and dancers. The results show that after the experiment, the calorie data consumed by the students in groups A and B, has a significant difference (P=0.01). After 12 weeks of teaching experiments, the situation of all abilities and independent learning conscientiousness of group A was significantly improved. A total of five advantages of AI dance teaching could be obtained through interviews. Therefore, the results of this paper provide new thinking and ways to realize basic dance education.

Abstract Haikai ink figure paintings are often equipped with localized deformation of the painting form, which brings inconvenience to the feature extraction of the paintings. Based on this, this paper proposes to utilize Contourlet transform to process ink figure images, and the low-frequency and high-frequency energy features in the ink figure paintings are classified by the Contourlet transform. Color space conversion and preprocessing of ink figure paintings, geometric reconstruction of ink figure paintings, expressed as a new composition of ink figure painting creation. Filter the LP filter in Contourlet transform, and test the image fusion effect using objective evaluation indexes, such as information entropy, joint information, mutual information, edge strength, and average gradient. Analyze the advantages of reconstructing images using Contourlet transforms. Apply the image aesthetic evaluation index to aesthetically analyze the ink figure painting based on Contourlet transform. The ink figure paintings processed by Contourlet transform have 0.0661~0.7124, 0.0668~0.5659, 0.0384~0.5657, and 0.0465~0.1782 enhancements in saturation E S, contrast E C, sharpness E R, and composition E M, respectively.

Abstract Silverdrum fish is popular among farmers because of its fast growth and easy rearing, so it is important to study the optimization method of the artificial breeding strategy of silverdrum fish stock. In this study, according to the requirements of the breeding environment of silver scat and the experience of experts, the flow rate, water depth, water temperature and light market are the key factors of the optimization model of silver scat breeding, and the multi-objective optimization algorithm NSGA-II is selected as the main method of the optimization model, and the intersection operator and the variational operator in the algorithm are improved, and then the SDR algorithm is proposed as a substitute for the Pareto dominance relationship to enhance the solving ability of the algorithm. The SDR algorithm was found to be the most suitable environment for silver drum breeding when the temperature, flow rate, water depth and light duration were set to 22.5℃, 0.21m/s, 1.7m and 15h, respectively. The results of the breeding experiments showed that the growth and gonadal development of silver drum parents in the optimized breeding strategy group were better than those in the original breeding strategy group, and the survival and growth rates of juveniles were better, so that the breeding effect and germplasm resources of silver drum were improved. This study can provide a theoretical basis and technical support for the decision-making of artificial breeding and germplasm resources improvement of silver drum stock in the future, and also lays a data foundation for the establishment and improvement of silver drum stock breeding farm.

Abstract The study establishes mixed-frequency vector autoregression (MF-VAR) model by combining the data of actual trade intensity, per capita energy consumption and per capita carbon emissions. The dynamic relationship between carbon tariffs and foreign trade under the transformation of low-carbon economy is analyzed through the descriptive analysis of sample data, unit root, cointegration, Granger causality tests, impulse response and ANOVA, and other econometric methods. The econometric results show that there is a long-run equilibrium relationship between trade intensity, per capita energy consumption and per capita carbon emissions. The three data variables have time series smoothness at 5% and 1% significance. There is 1 cointegration relationship between the variables at 5% significance level. The p-value of Granger causes is less than 0.05 and carbon tariff is the Granger cause of foreign trade in industrial goods. Impulse response analysis shows that the short-term impact of low-carbon economy on foreign trade is negative, but it gradually turns positive over time. The contribution rate of low-carbon economic development to foreign trade is around 16.1% in the short term and stabilized at around 26.2% in the long term. Constructing a synergistic policy of carbon mechanism and carbon tariff is of great value in accelerating the transformation and development of low-carbon economy and enhancing the total amount of foreign trade.

Abstract In order to understand the current situation of college students’ tennis sports and its correlation with depression, and to establish an effective solution to prevent college students’ depression. In this paper, the whole-group sampling method was used, and a questionnaire survey was conducted among college students using the tennis sports questionnaire, the depression self-assessment scale, and the SCL-90 scale. And then analyzed the effect of tennis sports on depression status through a multilayer regression model, and made a relative assessment of the effect of mental health interventions. The p-value of the overall score of tennis exercise is less than 0.01, and the differences between those who played tennis for less than 20 minutes and more than 60 minutes, those who were very dissatisfied and very satisfied with the effect of tennis exercise and those who were dissatisfied and very satisfied with the effect of tennis exercise are statistically significant, which indicates that tennis exercise not only has an improvement effect on the depression status of college students, but also, with the improvement of the time of playing tennis exercise and the effect of tennis exercise, the depression status of college students is improved. The above results indicate that tennis not only has an improving effect on college students’ depression, but also that with the improvement of the duration of each tennis exercise and the effect of tennis exercise, the improvement of college students’ depression will be further optimized. After the intervention of tennis learning and teaching, the scores of students’ mood-related factors decreased, and the decrease of “anxiety” factor was the most significant (P<0.01), which indicated that tennis had a promoting effect on students’ physiological and psychological health. Therefore, increasing the duration of each tennis exercise and improving the effectiveness of tennis exercises have certain guiding significance for improving college students’ depressive mood and mental health.

Abstract Multi-hop wireless communication networks occupy an irreplaceable position in many application areas by virtue of the advantages of network flexibility, easy deployment, low cost and expansion strength. In order to further optimize its network topology and improve the destructive resistance to malicious attacks, this paper uses the GCN model to learn the features and representations of the nodes in the graph, extracts the local and global features of the nodes, and uses a more complex function multilayer perceptron to fuse the features of the nodes, topology, etc., and optimizes the topology control method by judging the probability of the existence of each edge in the network topology. Simulation experiments on the power control and allocation performance of GCNTO are conducted under cellular network conditions, and the final convergence result of GCNTO exceeds 96% of the optimization result of the FP algorithm under different hyperparameters and number of training samples, as well as under the conditions of scaling up and scaling down the area and increasing and decreasing the number of users, which demonstrates the excellent convergence and scalability. After the model training of the two types of interference channels in advance, GCNTO is able to realize the accurate dynamic capture of topology and node information for untrained user channels and maintain the ideal data rate.

Abstract With the rapid development of UAV industry, autonomous UAV obstacle avoidance navigation has become a core problem in the field of UAV control. Based on recurrent neural networks, this paper proposes an LSTM-enhanced Layered-RSAC algorithm to construct a collaborative task execution and autonomous navigation system for UAV swarms. By constructing the autonomous navigation system of UAV, its accuracy is tested and the UAV operation situation index is examined. Through model training, the standard deviation of the a priori strategy with the best success rate of autonomous navigation is explored. The a priori strategy σ = 0.45 is taken as the initial value to verify the performance improvement of the Layered-RSAC algorithm. The results show that the Layered-RSAC algorithm reaches 90% navigation success rate at 50 training steps for the first time and stabilizes at 90% to 100% success rate after 100 training steps, which is significantly ahead of Prior-Policy, DDPG and SAC algorithms.

Abstract The face feature point detection algorithm has a very broad application prospect in the field of face recognition. In this paper, from the perspective of computer vision, the specific connotation of face feature point detection and face shape indexing with pixel coordinates is elaborated. The overall framework of cascade regression algorithm is built through training and testing, and the face pose changes are extracted based on pose indexing features and weak invariance to ensure that the algorithm can realize real-time face feature point detection. Train all levels of cascade regressors using data distribution statistics to allow the cascade regression framework to complete incremental learning and restore the position of real shape markers. Compare the cascade regression algorithm with existing face feature point detection methods, and verify that the cascade regression algorithm has high detection accuracy and fast detection speed in face feature point detection by pupil localization test and detection speed test. The detection rate of the cascade regression algorithm in the test is more than 90%, the pupil detection accuracy can be controlled at about 3 pixels, and the detection speed is about 87FPS, which is able to quickly and accurately recognize the face feature points, and it has practical and broad application prospects in real life.

Abstract High-speed railway service quality is a kind of experience and the degree of satisfaction of the experience when passengers consume passenger transport services, focusing on the process of service and the perceived satisfaction of passengers. This paper establishes the service quality evaluation index framework and system for high-speed railways by combining the concept of service quality for high-speed railways with the SERVQUAL model. The SERVQUAL evaluation model calculates the SQ score of each service factor to understand the change in service quality and combines the index weights and scores to calculate the recommendation degree of service quality optimisation of high-speed railways. The fsQCA method is introduced to construct the high-speed railway service quality influence model, and the data is obtained through a questionnaire to carry out a group analysis of the influence factors. The mean value of the evaluation score of high-speed railway service quality is -0.46, and the optimisation recommendation degree of the high-speed railway service quality optimisation indicators that need to be optimised is lower than -0.01. The coverage of the solutions in the group analysis is 0.872, which indicates that the five groupings are able to explain more than 87.2% of the high-speed railway service quality related cases in the sample. The data-driven service quality optimisation of high-speed railways needs to pay attention to the optimisation of basic service equipments and tools, and improve the dynamic response ability to better satisfy the needs of passengers in order to enhance their sense of access.

Abstract In order to realize the collision and fusion of Yi clothing culture and modern clothing design in Liangshan, China, this paper proposes the innovative fusion strategies of pattern cutting and color feature extraction for Yi clothing, respectively, and constructs a corresponding implementation model on the basis of artificial intelligence technology. Based on the UNet model, the pattern segmentation model is constructed by integrating multi-scale feature extraction and attention, embedding ASPP and ECA attention, and combining the Lovász-hinge loss function. The color network relationship model is constructed and optimized, using K-means clustering method to extract the image application color and other related color data, constructing and optimizing the color network relationship model. Using the Yi dress pattern cutting and color feature extraction technology, a series of clothes with the theme of “Yi Seeing and Loving” is designed by integrating the Yi dress cultural elements. Respondents think that the overall color and pattern matching style of the series of clothing designed in this paper is more inclined to gorgeous, traditional, coordinated, chic, modern and easy to match, and the evaluation value of the clothing style design and decorative detail design focuses on the interval of [3,5]. At the same time, the proportion of respondents who evaluated the degree of ethnic style in the [3,5] interval and the design effect in the [2,3] interval was as high as 80%.