|
|
- Integration and Innovation of Higher Education Management and Student
Training Mechanisms Based on Multi-scale Feature Fusion
Abstract: In the current educational reform wave, which prioritizes moral development as the fundamental goal and stresses the enhancement of core student competencies, aligning higher education management with student cultivation mechanisms is crucial to educational progress. The Lucas-Kanade (LK) optical flow algorithm is used in this paper to gather behavioral characteristics from student interactions within the cultivation mechanism. A multi-scale convolutional kernel approach is used to fuse these features both locally and globally. A multi-scale feature fusion module subsequently classifies and recognizes these features, with recognition accuracy optimized by a tailored loss function. This approach allows for the timely identification and analysis of students’ aberrant behaviors, which aids in prompt educational interventions. The multi-scale feature fusion model can effectively identify various types of aberrant student behaviors, which aids educators and institutional leaders in their management efforts, as revealed by experimental findings. Statistically, the model’s implementation led to significant improvements in classroom routine compliance, with pre-and post-test p-values in the experimental cohort showing a notable difference (p = 0.001; p < 0.05). The proposed multi-scale feature fusion model promotes the integration of management and training mechanisms in higher education while also supporting the development of students’ learning capabilities. This innovation sets a solid foundation for future educational advancements.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- Behavioral Analysis and Educational Management of College Students in
Universities by Integrating Multi-source Data
Abstract: Big data technology has brought new opportunities for the development of the education industry and can play a positive role in the management of student campus education. In this paper, the behavioral features in the extracted multi-source behavioral data of college students are dimensionality reduced using principal component analysis, and the behavioral features are clustered using density peak clustering and undersampling methods. The Adaboost algorithm is used to identify and classify each behavioral feature to obtain behavioral analysis data for students. Finally, the university’s education management system is constructed based on this behavioral analysis model. The behavioral analysis of students in a university shows that both consumption behavior and book borrowing behavior cluster into three categories. Among them, the type of students who clocked into the library with a medium frequency and borrowed the most number of books had the highest percentage (39.25%) among the students in this university. And there is about a 79.3% probability that students of the university with a high number of absences seldom visit the library. The study also showed that there was a significant difference (p<0.05) in the study behavior of students in both experimental and control classes after the application of the educational management system. The student behavior analysis and management platform proposed in this paper effectively promotes the development and progress of education management in colleges and universities and improves the efficiency of student management work and the scientificity and reliability of the results.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- Personalized Recommendation System for English Teaching Resources in
Colleges and Universities Based on Collaborative Recommendation
Abstract: In recent years, with the rapid development of the Internet and education informatization, online teaching has become a popular education mode in the information age, providing learners with very rich teaching resources. In this paper, we construct a personalized system and co-recommendation technology for English teaching resources, and we improve the traditional co-recommendation algorithm and propose hybrid recommendations. The performance of the system is evaluated experimentally to compare the effectiveness of the performance of the four systems. The improved recommendation algorithm is superior to the other three recommendation algorithms in each dataset in the system. The average grade mean of the experimental class assisted by the hybrid recommendation system in teaching English in colleges and universities in the latter two experiments is 27.54, which is higher than that of the comparison class of 25.33, and the T-value is 1.81>1.645. The improved personalized recommender system has good validity and stability in both performance and practical application.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- Multiple regression analysis of the mechanism of the role of
infrastructure development in rural economic growth
Abstract: The improvement of China’s economic level makes the society’s requirements and standards for infrastructure constantly improve. In order to effectively promote the rapid development of the rural economy, it is necessary to strengthen the construction of rural infrastructure. This paper provides a comprehensive plan for rural infrastructure construction and analyzes its mechanism of action on rural economic development in depth. Taking rural economic growth as the explanatory variable and infrastructure construction as the explanatory variable, the multiple linear regression model is chosen to analyze the impact of rural infrastructure on rural economic growth. The unknown parameters are estimated by the least squares method. The model is tested and modified based on the diagnostic methods of covariance expansion factor and other covariates to obtain the final results. Through empirical analysis, rural economic growth = -15.1935 + 0.184*rural transportation + 0.0983*rural education + 2.4923*agricultural science and technology + 0.3652*agricultural water conservancy, and agricultural science and technology has the greatest impact on rural economic growth. The local area can improve rural infrastructure in three aspects: investment strength, investment focus, and investment and financing mechanisms.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- Optimization study of anomaly detection algorithm in machine vision
inspection technology
Abstract: In recent years, target detection algorithms based on machine vision have been a hotspot in computer vision research. The You Only Look Once (YOLO) algorithm, as an excellent target detection algorithm, has played an important role in improving detection speed and accuracy with the improvement of the network architecture in its development process. This paper introduces the concept of integrated learning to the YOLOv5 network architecture, incorporating deformable convolution and attention mechanisms. It also chooses the focal EIOU loss function to replace the GIOU loss function, thereby addressing the issue of localization loss, prioritizing abnormally detected targets, and enhancing the detection efficiency of these abnormal targets. Finally, we examine the practical value of the improved YOLOv5 algorithm by testing its performance and applying it to real-world anomaly detection. The results show that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of performance and practical application advantages. In terms of performance, the classification accuracy of sea_person and earth_person in the improved YOLOv5 model is 37% and 26%, respectively, which is a significant performance gain overall. In actual application tests, the proposed method is more accurate than the traditional method. The accuracy is significantly higher.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- Improvement of Inventory Management and Demand Forecasting by Big Data
Analytics in Supply Chain
Abstract: Inventory management plays a very important role in the process of business operation, providing favorable backing for the smooth operation of production and sales. In this paper, LightGBM and PSO-LSTM models in big data technology are combined to improve inventory management and demand forecasting in supply chains. Then, the relationship between inventory, order, and forecast is elaborated, the two-level inventory cost components and the relationship between them are analyzed, the model constraints are formulated, and a mathematical model for two-level multi-cycle inventory control is constructed. Finally, the single demand forecasting model is compared with the improved model to explore the optimization effect of inventory management after the application of the LightGBM-PSO-LSTM model. The LightGBMPSO-LSTM model is the best fit and can be used for actual demand forecasting. After the optimization of inventory management, the inventory turnover ratio of Company H increased from 8.2 in 2022 to the maximum value of 9.2 in 2023, and the OTIF achievement rate of sales orders increased from 97.9% in 2022 to 99.3% in 2023. This paper provides a successful example of optimizing supply chain inventory management using big data analytics.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- Spatio-temporal correlation analysis of tourism urbanization patterns and
high quality ecological services
Abstract: Exploring the development of tourism-based urbanization and improving the overall competitiveness of regional tourism is a major initiative to promote the development of tourism in countries in China. In this paper, we take the urbanization pattern of tourism as the background, water yield, soil retention, and habitat quality as the factors, and use a bivariate spatial correlation model to explore the spatiotemporal association between the urbanization pattern of tourism and ecosystem services, so as to provide a reasonable reference for the development of the urbanization pattern of tourism from the perspective of ecosystem services. It was found that the construction of tourism urbanization pattern increased water production and soil retention by 17.07% and 23.08%, respectively, compared with 2000. In addition, habitat quality contained the highest ecological service value of woodland, which provided 81% of water production and 470.79 g·m−2 of carbon storage. Throughout the period, the spatial distribution pattern of soil conservation and habitat quality remained largely unchanged, and the synergistic trade-off relationship between ecosystem services demonstrated spatial heterogeneity. The interaction relationship was dominated by synergistic relationships, with a small number of districts and counties existing as a trade-off. The study measures how ecosystem services change over time and space in Province A. It also explains how trade-offs and synergistic relationships between ecosystem services work. Finally, it gives a good scientific basis for the growth of high-quality ecological services and tourism in a region that is rapidly urbanizing.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- Evaluation of the Effectiveness of Combining Virtual Reality and Augmented
Reality in Children’s Pharmaceutical Packaging Designs
Abstract: The rapid development of virtual reality and augmented reality technology has brought significant changes to many industries in modern society. In the field of product packaging design, the use of virtual reality and augmented reality technology in product packaging design is becoming more common in the field. Researchers are exploring the use of VR and AR technology in children’s drug packaging, employing augmented reality research that leverages natural features and artificial recognition to construct a model of children’s drug packaging that incorporates VR and AR technology. We analyze the impact of VR+AR on children’s drug packaging. Consumers’ main requirements for children’s drug packaging are bright, strong, practical, fun, novelty, and ease of understanding. The paper’s model, sample Z, ranks 5th in market competitiveness and 1st in technical competitiveness, outperforming competitors A, B, C, and D. This suggests that VR+AR children’s drug packaging still needs improvement in market competitiveness despite its high technical content. In descending order, the design evaluation dimensions score the VR+AR children’s drug packaging as follows: vivid picture (3.193)> texture (3.154) > easy to hold (3.142) > Moderate capacity (3.129) > simple and generous (3.105) > conform to the production process (3.047) > harmonious color (3.044) > convenient for transportation (3.028) > low production cost (3.012). The average score for each dimension of the VR+AR children’s drug packaging experience is approximately 4.46 points.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- Exploration of Intelligent Transformation Path of Traditional Cultural and
Creative Products Based on Internet of Things Technology
Abstract: More and more modern technology is applied to traditional cultural and creative industries, which enhances the technicality and novelty of traditional cultural products, and the fusion of culture and modern technology profoundly changes people’s way of life and learning. This paper explores the Internet of Things (IoT) technology and the intelligent transformation process of cultural and creative products and constructs cultural and creative products that integrate intelligent voice series. Specifically, a speech recognition model is integrated into the design process of cultural and creative products, and the designed model is applied to the design of intelligent cultural and creative products by analyzing the Hidden Markov Model (HMM), proposing a solution to the shortcomings of the HMM model, and choosing to perform noise reduction of speech signals through the fixed beam algorithm. Based on the speech recognition model designed in this paper, an intelligent cultural and creative product with a speech recognition function is designed, taking the ‘owl wine container’ from the Shanxi Museum as an example. The regression equation of the audience’s satisfaction with the product is Y (satisfaction) = -0.000263 + 0.208X1 (practicality) + 0.265X2 (innovation) + 0.253X3 (culture) + 0.271X4 (interactivity) - 0.296X5 (fun). This paper’s intelligent cultural and creative products have a greater impact on satisfaction due to their innovativeness and interactive function.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- A study on the application of neural style transformation technique in
personalized art creation
Abstract: In this paper, using the loss function of image stylization, combined with the development of image stylization based on deep learning, the proposed convolutional neural network in the style of image conversion fitting and overfitting response. Based on this, the multi-scale feature fusion method is chosen to train the style conversion network, with the help of deep feature extraction of the image for style conversion, reconstruct the multi-scale feature fusion image, and send it to the decoder for deep coding to realize the style conversion. To evaluate the effectiveness of the proposed multi-scale feature fusion style conversion algorithm, the content loss and style loss parameters of the algorithm are analyzed using the lightweight encoder and VGG encoder, respectively. Calculate the number of algorithmic model parameters and the amount of computation. Analyze the change in the iteration number of the personalized art style conversion process and select the performance evaluation index to evaluate the results of the personalized art style image conversion process. In the process of personalized art style conversion, the multi-scale feature fusion algorithm network proposed in this paper can basically reconstruct the original image after 1000 iterations. Personalized style image reconstruction has a PSNR of 25.26 dB when the number of iterations is 1000. With the deepening of training, the reconstruction effect becomes better and better, and the advantages of personalized art style image conversion applications are significant.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- A time-series analysis of the evolution of scientific and technological
concepts in the change of social thinking in modern China
Abstract: Exploring the evolutionary process of the concept of science and technology in modern Chinese society is conducive to the innovation of the current path of science education popularization and scientific and technological literacy enhancement. In this paper, we construct a sampling selection algorithm using Bayesian and MCMC and then design a parameter derivation model. Assuming the model prior distribution hyperparameters and using the likelihood function to derive the specific form of the posterior probability distribution, an approximate integral calculation is carried out in order to directly derive the parameters to be estimated, and the BVAR-MCMC model is constructed. Using the model to analyze the temporal changes in the evolution of scientific and technological concepts in modern China, it is finally found that the changes in social and technological concepts during the Republican period were even stronger than those in the late Qing period. Influenced by the increasing scale of cross-cultural exchanges due to the gradual opening up of the gateway, cross-cultural open exchanges have the highest impulse value and the longest duration on the evolution of scientific and technological concepts, with the impulse of the Late Qing and the Republic of China phases above 1, and the duration of the positive influence covers the whole time series. The impulse values of the late Qing and the Republic of China are 2.68 and 3.59, respectively, and the education popularization rate, economic development level, the speed of technological life progress, and the degree of civilization and freedom of the social atmosphere have different degrees of influence on the evolution of scientific and technological concepts in the time series of modern history. This study innovates the statistical research method for modern history and conducts a pioneering exploratory study on the evolutionary process of modern social ideas change.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- Correlation Mining-Based Strategies for Improving the Quality and
Efficiency of Financial Data Center Operation, Maintenance, and Monitoring
in Cloud-Native Models
Abstract: At present, the daily operation and maintenance of large-scale data centers such as banks in China, due to a variety of reasons, often brings about the problem of unexpected events that are difficult to locate. In order to ensure that the systems running in the data center work efficiently, this paper proposes a method for improving the operation, maintenance, and monitoring of financial data centers based on the cloud-native model. First, we sequentially cleanse and process the financial center data to eliminate any negative impact and generate a time-trending correlation of financial attributes. We then apply association mining to data center operation and maintenance, using stock information as an example to analyze the operational results in stock trading transactions. The result of correlation mining is component B index (up)⇒ component A index (up), support = 12/100, confidence = 12/19, which indicates that in 100 trading days, the number of days that the component B index and the component A index rise together is 12 days, while the number of days that the component B rises alone is 19 days. In the case study examining the impact of association mining in stock trading, on March 15, 2022, the stock price experienced a rise from 11.456 to 11.498 within a mere 0.1s. The financial data operation and maintenance system, using association mining, identified this as “abnormal,” demonstrating the model’s successful detection of abnormal behavior.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- Exploring the Influencing Factors on the Quality of Japanese Language
Classroom Teaching in Colleges and Universities Based on Structural
Equation Modeling
Abstract: In this thesis, the Japanese language major in the College of Foreign Languages of University J was selected as an example, and the research method combining quantitative questionnaire and qualitative research was used to investigate the development trend and current situation of Japanese language teaching. Structural equation modeling was used to analyze and hypothesize factors that affect the quality of Japanese classroom teaching. Data was collected, processed, and analyzed with relevant regression statistics to study the statistical regularities among variables with correlations and to verify the research hypotheses proposed in this study, as well as the correlations. The results showed that there was an extremely significant causal relationship (Sig. < 0.01) between teacher level: professional ethics and teacher teaching, teacher professional knowledge, resource inputs and teaching content, and teacher professional competence, teaching content, and teacher teaching. Student level: there is a significant positive effect (Sig<0.01) between students’ personal qualities, student learning and learning outcomes, learning inputs, and student learning and learning outcomes. Industry level: there is an extremely significant causal relationship (Sig<0.01) between industry needs and teaching objectives, resource inputs, teaching content, and teacher teaching.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- Analyzing the Impact of ESP Teaching Models on Enhancing Non-English
Majors’ English Application Skills
Abstract: The development of globalization has led to the rising demand of society and enterprises for the English application ability of students in colleges and universities, and the enhancement of the English application ability of non-English majors has become the key research direction of English teaching in colleges and universities under this trend. In this paper, we study this issue, highlighting the urgency of innovating the English teaching mode for non-English majors through the current situation of the passing rate of the English application ability examination and the main problems of the cultivation of English application ability of non-English majors. Gray correlation analysis is used to study the correlation between the English teaching mode, job requirements and other factors and the English application ability of non-English majors to provide data support for the introduction of ESP teaching mode. The ESP teaching mode is proposed, the teaching experiment is designed, and the results of the experimental and control classes are tested before and after participating in the experiment to assess the role of the ESP teaching mode in improving the English application ability of non-English majors as well. In the study of students’ satisfaction with the teaching of application abilities under the ESP mode, the overall satisfaction is high. Non-English majors are rated 11.87% more satisfied with the teaching effect under the ESP teaching model than English majors.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- An Exploration of the Application of Augmented Reality Technology in
Improving Interactivity in Physical Education Teaching and Training
Abstract: This paper elaborates on the technology related to augmented reality interaction, introduces in depth the augmented reality device HoloLens, including its composition and the principle of generating indoor three-dimensional data, and designs a teaching interaction model of augmented reality fusion learning environment. The Kinect somatosensory device is introduced into the teaching interaction model, and virtual interactions between virtual objects and real objects are accomplished by judging the human body’s position and posture changes in real time. Group experiments were conducted to examine the learning effect of augmented reality-based technology with or without interactive behavior in terms of interest in sports learning. According to the findings, the augmented reality physical education interactive teaching experiment resulted in a 23-point increase in the average value of physical education knowledge mastery level test scores. Further one-way ANOVA was conducted and the p-value of the scores of group B of the experiment with interaction before and after teaching was 0.001, which is less than 0.05. Augmented reality sports interactive teaching can significantly enhance students’ interest in sports learning, attention to sports, and so on. Therefore, the augmented reality sports interactive teaching technique mentioned in this paper serves as an effective way to improve students’ performance and interest in sports education.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- Optimization of coal chemical process parameters and energy saving and
emission reduction based on genetic algorithm
Abstract: Coal is an important energy resource. How to utilize it efficiently and cleanly is a hot topic nowadays. In the coal gasification process, the process parameter indexes have a significant impact, and the uncertainty of these factors will lead to a decrease in the quality of gas production. Therefore, in this paper, the uncertainty of process parameters is considered, and Aspen plus software with the Monte Carlo method is used to simulate the coal chemical process and measure the effect of uncertainty of process parameters on the yield of the coal gasification process. On this basis, in addition, coal flow rate, pressure, and steam/oxygen are taken as the process parameters and optimized, and three sets of multi-objective optimization models are established with gas calorific value, gasification efficiency, and gas yield, respectively, which are solved by improved multi-objective genetic algorithm based on crossover operator and variational operator to obtain Pareto curves, so as to adjust the parameter values according to the actual needs. The results show that the fluctuation of pressure has a big influence on the carbon conversion rate and gasification efficiency, and the carbon conversion rate and gasification efficiency can be made more stable by controlling the change of pressure. The improved genetic algorithm NSGA-II can reach the actual optimal objective function value in both high and low iteration times, providing the required parameters for the decision maker, and the optimal program results in TEC of 402,758 kW and CO2 content of 0.12%, which is effective in energy saving and emission reduction.
PubDate: Mon, 05 Aug 2024 00:00:00 GMT
- Research on Target Detection and Recognition Algorithms in Remote Sensing
Images
Abstract: With the continuous improvement of computer vision and deep learning technology, the target detection methods of remote sensing images are also expanding and diversifying. In view of the shortcomings of the current object detection and recognition algorithms in terms of accuracy and versatility, this paper introduces the reverse scale transfer layer and feature pyramid (FPN) modules and applies the attention models of channel attention mechanism and spatial attention mechanism to each module of the convolutional neural network, so that the feature layer can obtain accurate and comprehensive prediction information, and finally proposes a remote sensing image object detection algorithm DCYOLOv7 with high accuracy. Compared with the benchmark model, the accuracy of the algorithm on small, medium, and large targets is improved by 14.69%, 4.14%, and 5.19%, respectively. The DC-YOLOv7 algorithm is improved by 10.15%, 12.16%, 13.18%, and 14.8% compared with the mAP, AP50, AP75, and AR100 of the benchmark algorithm, respectively. DC-YOLOv7 has a better detection application effect than the classical algorithm in the military aspect. The effectiveness and versatility of the target detection and recognition algorithm in the remote sensing images presented in this paper have been verified.
PubDate: Tue, 02 Jul 2024 00:00:00 GMT
- Research on the Interface Design of Smart Tourism APP for the Elderly
Based on KANO-QFD
Abstract: This paper analyzes the integration process of the Kano model and the QFD model, combines the advantages of the two, and proposes a method of obtaining the importance of user requirements based on the integration method of the QFD and Kano model. Organize older people’s smart tourism needs and use the Kano model analysis method to categorize each demand attribute. Obtain the self-scoring importance degree of the elderly demand, calculate the satisfaction index of the elderly demand, and get the comprehensive ranking of the importance degree centered on the elderly demand according to the SII, DDI, Ti, and K values of the demand elements. Clarify the elements of smart tourism APP interface design services, establish the relationship matrix between the needs of older people and the service index, and build the service quality house of smart tourism APP interface design for the elderly. Use the smart tourism APP functional interface planning strategy to present the functional design of the APP interface. It can be found that those with higher importance ratings are mainly basic functional requirements, attraction guides, and personalization center, of which the four importance indexes of U6 personalization center are U61=4.72, U62=4.75, U63=4.16, and U64=4.23. The calculation results of the quality house show that the importance of the service elements of the interface design of the APP for senior citizens’ smart tourism is ranked as APP content professionalism, service attitude, recommendation rationality, and service quality. Degree > service attitude > recommendation rationality > after-sale reliability.
PubDate: Tue, 02 Jul 2024 00:00:00 GMT
- Exploring the application and value of martial arts in modern society by
combining double difference models
Abstract: Properly acknowledging the application and value of modern Chinese wushu is essential for its ongoing survival and development and plays a pivotal role in shaping its future direction. This analysis delves into the application value of wushu within contemporary society, leveraging its unique characteristics and advantages. Specifically, the exploration is structured around four principal domains: competitive sport, tourism, technical labor, and martial arts training. These dimensions collectively underscore the multifaceted impact and significance of wushu in the modern era. Then, based on relevant theories, research hypotheses, and research variables are determined, a double difference model oriented to the socio-economic value of wushu is constructed, and the value of wushu is analyzed with the support of the model by combining relevant research data. The conclusion presents that the panel data of FDI, Service, and Urban are smooth at a 1% level, while the panel data of GDP and Events are smooth at a 5% level. The p-values of model (1) and model (2) are 0.206 and 0.388, respectively, which indicates that martial arts have a certain promotion effect on the development of the local economy.
PubDate: Tue, 02 Jul 2024 00:00:00 GMT
- Modeling Artificial Intelligence in Real-Time Collaborative Piano Playing
Systems
Abstract: Due to the challenges associated with mastering fundamental piano playing techniques, the inefficiency of self-guided learning, and the prohibitive cost of one-on-one instruction, many novices abandon their musical pursuits prematurely. Our research addresses these issues by enhancing music feature extraction methods through artificial intelligence modeling and developing a piano-playing ability evaluation system. This system leverages an attention mechanism and an LSTM neural network model to assess a player’s abilities based on rhythm, thematic prominence, and musical expression within various levels of piano scores. By analyzing sample tracks from the Thompson Simple Piano Tutorial, our system demonstrates robust performance, achieving an overall F-Measure above 0.9 with an average value of 0.9641. These results indicate that the evaluation system offers precise assessments and can significantly aid piano instruction, providing learners with reliable feedback on their progress.
PubDate: Tue, 02 Jul 2024 00:00:00 GMT
|
Open Access journal
