bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on the Integration of Traditional Dress Craft Teaching and Online Learning Platform Based on AR Technology
Authors: Gao Xuan 
Published: December 2024

Abstract
The use of augmented reality (AR) technology in the field of education is becoming more and more common, and this study combines it with an online learning platform, applies the AR visual interaction function to the teaching of traditional dress handicrafts, and builds an online learning system of AR traditional dress handicrafts. Through the evaluation of the teaching quality of this online learning system, the performance of the AR traditional dress handicraft online learning system in teaching is revealed. The immersion experience, learning engagement, and system satisfaction scales were used to explore the student’s sense of using the online learning system constructed in this paper after testing the teaching effect. In the evaluation of teaching quality, the mean values of the three-level indicators were between 3.68 and 4.34, of which the mean values of lecture-practice fit (3.980), learning habits (3.680) and inquiry learning (3.740) did not exceed 4. The mean values of the first-level indicators and the second-level indicators ranged from 4.017 to 4.272, and from 3.725 to 4.314, respectively. The mean value of the cognitive posttest of the subject students was 87.591, and the mean value of the pretest was 32.485. The significance of Sig. The value was 0.000, and the difference was significant. The mean values of the dimensions of students’ immersion experience, learning engagement, and system satisfaction were between 3.92 and 4.09. The students reported feeling good about using the online learning system in this paper.
Research on collaborative task scheduling and cooperative control strategy for construction robots based on improved particle swarm optimization algorithm
Authors: Chen Jin , Li Xiaoliu , Zhou Yulong , Liu Tao , Li Ruiping 
Published: December 2024

Abstract
A research program based on collaboration and cooperative control of construction robots is important for the development of the construction industry. This paper aims to develop and design a program based on collaborative construction robots and cooperative control technology to improve construction productivity. We construct a task planning and execution system for construction robots under distributed architecture, design the structure of the information management module, incorporate a hybrid network topology combining star and ring, improve the BEB algorithm to solve the channel collision problem, and form a multi-robot cooperative communication network system. Establish a multi-robot coordination model based on task division, adopt the idea of two-level division, propose a hierarchical distributed coordination method, and establish a hierarchical distributed coordination system framework. Use linearly varying inertia weights and acceleration factors to balance the global exploration and local search abilities of particles to form an intelligent path planning algorithm for construction robots. The simulation experiment environment is chosen, and each algorithm’s performance is analyzed. The simulation experiments involving collaboration and cooperative control of construction robots demonstrate the efficiency and practical application value of the optimization scheme designed and proposed in this paper.
Research on Behavioral Prediction and Cooperative Control Strategy of Construction Robots Based on Deep Learning
Authors: Zou Haona , Chen Jin , Li Ruiping , Wang Haobo , Wu Shun 
Published: December 2024

Abstract
This paper combines game decision-making and learning decision-making models to learn all possible types of strategies in robot behavior, describes human joints as a tree diagram structure through pose estimation, uses dynamic programming algorithms to derive joint information, extracts and models robot behavioral pose features, and identifies the action behaviors of construction robots. Through path tracking and other controls, robot behavior can be controlled to achieve the effects of construction robot behavior prediction and cooperative control. Set up simulation experiments to collect and preprocess the behavioral data of the construction robot, identify its behavior, predict its intent, and assess the safety risk of the construction robot’s action route. The robots constructed in this paper are put into the project, and the safety management input calculates the safety management efficiency. After adding the loss function to the model, the precision, recall, and F1 value mean of the construction robot are improved by 5.895, 5.461, and 5.765, respectively, and the derived safety management efficiency of the construction robot construction is 70, and the input of the construction robot brings a higher level of safety management to the construction project.
Flexible Manufacturing Line Design and Digital Twin Simulation for Sheet Metal Processing of Complex Parts
Authors: Li Chunhui , Ding Haitao , Huang Junsheng , Zhang Huiju 
Published: December 2024

Abstract
Traditional sheet metal processing enterprises are facing problems such as upgrading manufacturing systems, lower material utilization rate, and lower efficiency of CNC machining. In this paper, we design a flexible manufacturing production line for sheet metal processing of complex parts, use the DQN algorithm to optimize the scheduling of each part of the work in the production process of the production line, and optimize the adaptability of the DQN algorithm by using the action selection strategy and the reward function and so on. Then, the digital twin simulation system is constructed to simulate the process of a flexible manufacturing production line for sheet metal processing. The results show that the DQN algorithm can effectively assign the corresponding processing machines and processes to the workpieces in the production line, and the digital twin simulation system can also accurately simulate the actual situation of the production line. Meanwhile, it is found that the optimized production line can reach 87.52% of the production beat balance rate, which is 23.64% higher than the original production line balance rate, and it can improve the average utilization rate of the main operating units in the production line. The optimized production line proposed in this paper can effectively solve the practical problems in sheet metal production lines, improve the mobility of the production line and corporate efficiency, and provide new ideas for the lean production management of large-scale machinery manufacturing enterprises.
Technical Application of Artificial Intelligence-Based Voice Interaction Control and Intelligent Scheduling for Throwing and Retiring Reclosing Gates
Authors: Bai Yiming , Lin Xiongfeng , Peng Shifeng , Ruan Zhijie 
Published: December 2024

Abstract
Speech recognition, as an important carrier of human information exchange, is characterized by ease of use and high efficiency, and is an excellent choice to act as a medium of information interaction between humans and machines. In this paper, with the background of the application of throwback reclosing in the field of electric power, a speech recognition model is constructed using the DTW algorithm. Subsequently, on this basis, an intelligent dispatching command generation system integrating speech recognition, generation, and interaction systems was built, and the functional characteristics of the intelligent reclosing model were characterized through simulation experiments. After 40-50 rounds of training, the Loss curve of the model in this paper settles to about 0.16. After training for 100 rounds, the word error rate stabilizes at around 7%. In the call corpus, the speech recognition system constructed in this paper has a error rate of 1.124%. The recall in the Scheduling instruction corpus and Scheduling telephone corpus is 0.982 and 0.947 respectively. And, the model in this paper has better performance in terms of the number of errors in speech recognition samples and the word error rate of specialized vocabulary. In addition, the model in this paper can normalize the correlation of electricity consumption between different time points under abnormal conditions. In conclusion, the model in this paper is both efficient and stable in the power field, especially in the application of reclosing technology.
Research on Agricultural Product Marketing Education Models and Management Strategies Driven by Internet Technology
Authors: Piao Chensheng , Yu Linling , Li Xuefeng 
Published: December 2024

Abstract
Internet technology is bound to be a big opportunity for agricultural marketing, how to effectively use the Internet technology innovation of agricultural marketing for the current focus of the research direction. The paper examines agricultural product marketing that is driven by Internet technology from the perspectives of agricultural product marketing mode innovation and marketing education mode innovation. The design of a mixed teaching mode for agricultural product marketing aims to cultivate talents in agricultural product Internet marketing technologies. The analysis of the development of agricultural product marketing education mode uses a comparative method and is divided into two stages: online marketing teaching and overall marketing mode teaching. The improved RFM model is used to solve the value of agricultural product customers, and the consumption characteristics of customers of different age stages are found. Among the four types of customer groups, 25-40-year-olds accounted for the highest proportion in the developing and maintaining customer groups, and 40-55-year-olds and 10-25-year-olds accounted for the highest proportion in the low-value customer and high-value customer groups. Agricultural product marketers should focus on customers under 55 years of age as the target of agricultural products, focusing on services and implementing precision marketing of agricultural products.
Optimization Study of Youth Ice and Snow Sports Training Data - Analysis of TensorFlow Platform Based on One Belt and One Road Region
Authors: Wang Ying 
Published: December 2024

Abstract
Technology-enabled snow and ice sports data optimization facilitates the development of snow and ice sports training strategies and provides support for ensuring the safety and enhancing the training effect of youth snow and ice sports. This paper designs a system to collect and track snow and ice sports data. It is designed to extract electromyographic signal data from snow and ice sports training through a high-frequency wavelet function. A clustering algorithm is used to process and optimize snow and ice sports training data, and then a sports feature data mining model is established for snow and ice sports auxiliary decision support. The deep learning TensorFlow framework was used as the basis to realize the construction of a generalized dynamic fuzzy neural network, and a variable sliding window was used for model learning in order to realize the accurate prediction of the training load of youth snow and ice sports. The GD-FNN model predicted the muscular strength level of snow and ice sports training with a relative error of only 1.74%, and it also accurately predicted the blood oxygen saturation. The RMSE value of blood oxygen saturation was only 0.68. Under the personalized cyclic impedance training mode, the hemoglobin content of the experimental group was 152.64±8.76 g/L, which was significantly different from that of the control group at the 1% level. Relying on the training data of youth ice and snow sports in the Belt and Road, clarifying the physiological conditions of athletes in different countries can help to enhance the synergistic improvement of the ice and snow sports level in the Belt and Road countries.
Automatic essay scoring for natural language processing: feature extraction and scoring models
Authors: Lu Jia , Ni Pingli 
Published: December 2024

Abstract
Automatic essay scoring technology is an important application in the field of natural language processing, which combines machine learning, deep learning, and other technologies to automatically assess the quality of essays through computer algorithms. This paper combines the features of convolutional neural networks and long- and short-term memory neural networks to create the MLSN neural network model. The automatic scoring of essays using local semantic features, global semantic features, topic layer features, and shallow linguistic features aims to improve the fairness and accuracy of scoring. The clustering of 810 essay samples into 6 classes in this paper facilitates the subsequent analysis of the experiments. The model in this paper converges successively in terms of loss, accuracy rate, recall rate, and F1 after being trained on 20–40 batches. At approximately 0.08, 0.93, 0.91, and 0.88, respectively, their values stabilized. The evaluation indexes of this paper’s model have all significantly improved compared to the comparison model. This paper’s model has improved the QWK scores of essay samples for different topics to varying degrees compared to the comparison model. When combining multi-level semantic feature modules, the ablation experiment reveals a 6.91% improvement in the scoring effect of this paper’s model compared to the topic-only semantic feature module. The above experimental results fully demonstrate the superior performance of this paper’s model, suggesting that it has better applications in automatic essay scoring.
Research on Dynamic Adjustment of Enterprise Resource Integration and Strategic Management Based on Cloud Computing Optimization
Authors: Wang Geng 
Published: December 2024

Abstract
The extent to which modern enterprises utilize their resources significantly influences their strategy formulation and even raises questions about their ability to remain competitive in the market. This paper finds the best way to use traditional enterprise resources by using cloud computing. It does this by creating a multi-objective scheduling model to combine enterprise resources and using a stochastic-aware real-time task scheduling strategy to make the most of the combined enterprise resources and raise their utilization rate. Based on the dynamically changing enterprise resource information, a strategic integration model is constructed based on the dynamic resource view to adjust the enterprise development strategy in real-time. Company A, an automobile production enterprise, has been selected to conduct an example study to examine its resource integration and strategic management effect after experiencing cloud computing optimization. The deployment time of enterprise resources has increased after cloud computing optimization, significantly enhancing the efficiency of resource integration and deployment. In the small-scale workflow, the enterprise resource utilization rate achieves a 50% improvement, while in the large-scale workflow, the resource utilization rate exceeds 40%. Compared to the financial status of Company A before and after the optimization, its solvency, operating capacity, and profitability have significantly improved after the adjustment. The satisfaction scores of Company A’s management team on the five dimensions of quality of resource integration, efficiency of resource deployment, speed of response to market feedback, familiarity with the market, and reasonableness of the strategy are all above 4, indicating promising results in resource integration and strategic management.
Modeling the Impact of Cultivating Entrepreneurship and Creative Skills on Future Career in Music Education
Authors: He Rui 
Published: December 2024

Abstract
The current trend of mass entrepreneurship and innovation encourages college students to approach the new opportunities and challenges of employment with a positive mindset, while also expanding the opportunities for music majors to engage in “double creation.” The article establishes a model of music education based on the double helix model and outlines the competence structure and framework necessary for cultivating music talent. To quantify the cultivation of entrepreneurship and innovation ability in music education, this article introduces the Rasch model to assess and analyze the level of students’ entrepreneurship and innovation ability. To analyze the importance of entrepreneurship and innovation capability in future career planning, the SEM for parameter estimation using PLS is introduced to construct a model of influencing factors. Through examples, we analyze the quality of students’ dual-venture level, which integrates entrepreneurship and innovation abilities in music education, and explore the degree of influence this level has on career planning. The mean value of the measure (in logits value) of students’ bi-initiative level is 3.463, placing them at level IV overall. The study found that the cultivation of entrepreneurship and innovation ability in music education significantly influences future career planning, with path coefficients of 0.572 and 0.337, respectively. The integration of entrepreneurship and innovation-based teaching content in music education can help enhance students’ ability to plan for their future careers and contribute to the high-quality development of music professionals.
Design and Practical Effectiveness Evaluation of Intelligent Teaching Platform for Music Education in Colleges and Universities
Authors: He Rui 
Published: December 2024

Abstract
The development of the Internet, artificial intelligence, and other technologies has put forward new requirements for intelligent music education in colleges and universities. This paper utilizes Internet technology to construct an intelligent teaching platform for music education in colleges and universities, incorporating the intelligence of music appreciation, online examination, and music knowledge learning. The forward neural network multi-feature fusion algorithm in the music appreciation module constructs the music emotion classification model, arranging the content based on the appreciation content and emotion type. The online examination module uses the simulated annealing method to improve the genetic algorithm, which then intelligently groups papers. The method of hierarchical analysis is used to quantitatively and qualitatively analyze the students’ satisfaction and design evaluation indexes for music-intelligent teaching platforms from the perspectives of teaching content and teaching quality. The music education classroom of a southern university is selected to apply the intelligent teaching platform for testing, and as the number of students gradually rises to 300 and above, the stability of the platform still performs well. Simultaneously, the evaluation results of the teaching platform indicate that the teaching quality and grouping rationality index scores are 8.94 and 9.02, respectively. These scores indicate that the platform significantly meets the students’ needs in terms of teaching effectiveness and grouping rationality.
Pattern Recognition Based Music Style Recognition and Teaching Application in Higher Education Music Education
Authors: Yue Qiannan , Wang Lin , Luo Jia 
Published: December 2024

Abstract
This paper presents a summary of a range of characteristic parameters that define the tone features. This is achieved by studying the time-frequency and frequency characteristics of music signals with different instrumental timbres, and it represents the characteristics of the music in various frequency bands and time domains. The optimized DTW pattern recognition algorithm achieves the classification of music styles. The conducted experiments clearly recognized several basic violin bowing styles. Jazz’s classification and recognition effect is 80% accurate. The accuracy rate of the music brief spectrum recognition exceeded 95%. The teaching method based on music pattern recognition has a significant teaching effect in the knowledge and skill dimensions, with a Sig. value of 0.001.
Research on the Design and Practice of Smart Elderly Public Service Network Architecture in Ethnic Areas
Authors: Zhang Jing 
Published: December 2024

Abstract
In the context of the “Internet +” era, smart pension services have become an important way to solve the problem of elderly pensions in ethnic areas. This paper analyzes the feasibility of smart elderly care for elderly individuals in ethnic areas from different perspectives. It puts forward the main needs of the elderly service in ethnic areas, takes the welfare pluralism theory and resource dependence theory as the theoretical basis of the smart elderly public service system, and understands the smart elderly public service system from the three levels of technology, service and organization. Combine intelligent alarm technology for elderly falls with the Internet of Things technology to build a smart public service system for elderly people. Improve the intelligent sensing technology from the perspective of environmental perception and use Michelson fiber optic sensing technology as the medium for elderly fall signal detection. Test the main control and communication modules of the IoT platform. Analyze the role of improved sensing technology in recognition of fall actions, and increase the forward fall alarm rate to more than 90%. Research and statistics on the degree of satisfaction of the smart elderly public service system for elderly care and medical rehabilitation needs. The demand for convenient elderly maintenance in ethnic areas in the smart elderly service system accounts for 39.54%, which is lower than the 46.88% registered appointments. The smart elderly public service system can satisfy the life needs of the elderly to a certain extent, but there is a need to further increase the elderly’s knowledge of the smart elderly public service system in order to effectively satisfy the elderly services in ethnic areas.
Research on Automatic Sculpture Morphology Generation and Artistic Aesthetics Evaluation System Based on Machine Vision
Authors: Wang Chunxin 
Published: December 2024

Abstract
Sculpture has become a work of unique artistic charm by virtue of its rich visual aesthetics and unique carving method. The combination of computer vision and sculpture art has become a hot research topic in the field of art. Based on this, the article concludes the research on automatic morphology generation methods based on machine vision. Firstly, we use the morphology recognition machine to recognize and process the sculpture image, attach the source model to the background target model grid and fuse it, and generate the final sculpture model through the grid parametric mapping between the source model and the target model. Then, the automatic sculpture form based on machine vision is selected as the research object, and the fuzzy comprehensive evaluation method is applied to fuzzy evaluate the selected evaluation indexes. The fuzzy comprehensive evaluation method is used to evaluate the artistic aesthetics of the automatic sculpture form based on machine vision. The comprehensive target layer vector is calculated to be B = WR = (0.1713 0.1084 0.0772 0.0173 0.0143), and the final evaluation value is 27.527, and the evaluation grade is very good.