bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on CNC programming and machining process based on CAD/CAM technology
Authors: Zhang Shuling , Bai Jie 
Published: February 2024

Abstract
Focusing on CNC process data, this paper provides an in-depth analysis, characterization and mining of macro machining processes by associating CAD and CAM models. The article introduces a Bayesian inference method to construct an association relationship between the manufacturing features of the query part and the macroscopic process of the manufactured part, which avoids the need for a direct similarity comparison between the geometry of the query part and the part to be manufactured. In addition, the study calculated the overall similarity between the query part and the CNC programming process data instances through the guidance of the process skeleton, thus realizing the precise evaluation from the CNC programming perspective. The algorithm for generating tool trajectories in multi-axis CNC programming is analyzed. By selecting representative surfaces and performing tool processing calculations, this study explores the process planning and tool parameter selection of usual parts of surfaces in multi-axis CNC programming machining. It optimizes the nonlinear error in tool trajectory processing by combining CAD/CAM technology. The results show that the optimized process proposed in this paper significantly improves the machining efficiency by 36.94%. In the cutting at tool corners, the maximum cutting force generated by the optimized machining process is only 190.3N, which is only 67.2% of the leading cutting force of the Cimatron process. In addition, when the engagement frequency is 780 Hz, the optimized process proposed in this paper has the smallest vibration amplitude, which is only 21.4% of the Cimatron strategy. Therefore, this study significantly improves the machining efficiency while ensuring the machining quality, which has important practical application value.
Optimization of constraint rules for power grid topological relationships based on knowledge graph techniques
Authors: Wang Yu , Wang Wei , Wei Jie , Liao Wentao , Mo Liangyuan , Yang Jing 
Published: February 2024

Abstract
In this study, the expressive ability and accuracy of the constraint rules for grid topological relationships are effectively improved by introducing the knowledge graph technology. The TransE model is used for training, focusing on the strategies to eliminate single-node voltage overruns in single-line and multi-line open scenarios, and the characteristic relationship between co-matching coefficients and structural robustness is deeply analyzed, aiming to improve the grid’’s interference resistance by optimizing the structural robustness. The results show that the single-line high-quality scheme has six effective solutions, including lines 28-29, 26-29, 26-28, 21-22, and 2-3. In the multi-line high quality scenario, there are 11 effective solutions, such as lines 28-29, 21-22, etc. Further analysis shows that the robustness of the grid structure is better and the spreading of the outage area is relatively slow in the event of a fault when the co-matching coefficients r are −0.22374 and −0.14575, respectively. In particular, when r = −0.22374, the grid’’s robustness remains above 0.3 even when the number of nodes exceeds 30. The comprehensive optimization framework proposed in this study is expected to provide more reliable and efficient topological relationship constraint rules in power systems, which will provide strong support for the operation and management of power grids, and significantly improve the stability and security of power systems.
Optimization of energy dispatch for commercial and industrial users based on demand management strategies
Authors: Xie Yuzhe , Wu Xiuling 
Published: February 2024

Abstract
With the acceleration of power marketization, there is a growing demand for optimization of power demand management and energy dispatch by industrial and commercial users, especially in achieving energy consumption efficiency and cost reduction. We adopt a two-layer optimization model combined with a time-sharing tariff system to efficiently allocate electricity resources by constructing an electricity demand response model for industrial and commercial users. The study results show that after implementing time-of-day tariffs, the cost of electricity for industrial and commercial users has been reduced by 15% on average. At the same time, the grid company’s revenue from electricity sales has been enhanced by 10%. In addition, a customer satisfaction survey found that customer satisfaction with the optimized power dispatch scheme increased by 20%. The study’s conclusion confirms that through scientific demand management and scheduling strategies, we can effectively improve the utilization efficiency of power resources and reduce costs, while ensuring customer satisfaction and achieving a win-win situation for supply and demand.
Research on Production Scheduling of Industrial Big Data for Internet of Things Based on Dynamic Planning Algorithm
Authors: Zhang Nan 
Published: February 2024

Abstract
In this paper, the optimized Dual Heuristic Dynamic Programming (DHP) algorithm is studied in depth, compared and analyzed with the other three algorithms through experimental simulation, and the DHP algorithm is applied to different industrial accurate scheduling tests to explore the loads of the industrial equipment during scheduling operation. The simulation results show that the dual-heuristic dynamic programming DHP algorithm has a significant decrease in both the mean waiting time (MWT) and the mean response time (MRT), where the highest peak value of the mean waiting time is only 0.06901 μs, and the highest peak value of the mean response time is 0.24493 μs. In industrial accurate production scheduling, the improved DHP algorithm performs the best for textile industry’’s task scheduling with the best performance. Specifically, the task scheduling completion time for the textile industry is 4522 seconds when the number of automated guided vehicles (AGVs) is the minimum value of 3. In comparison, the completion time is 8541 seconds when the number of AGVs is the maximum value of 15. The textile industry shows the fastest task completion time compared to the other industry types for different settings of the number of AGVs. In addition, the analysis of the operation of the equipment engines shows that although there are high and low torques during operation, the Torque of most of the engines stays within the range of 29 Nm to 30.7 Nm, which is generally stable and has little impact on the performance of the equipment. The improved dynamic programming algorithm proposed in this paper has effective application potential in industrial extensive data production scheduling, which can improve the efficiency and responsiveness of production scheduling. It has important practical significance for industrial production management.
Study on the Application of Improved Deep Convolutional Neural Network Algorithm in Broken Information Recovery
Authors: Zhou Sheng 
Published: February 2024

Abstract
This study on the fusion of deep convolutional neural network (CNN) and extended short-term memory network (LSTM) aims to improve the efficiency and accuracy of broken information recovery. The challenges faced by traditional information recovery techniques are addressed through improved algorithms. The research methodology includes constructing CNN models to automatically extract features and combining LSTM networks to process complex time-series data. We conducted a detailed experimental evaluation of the CNN-LSTM fusion algorithm, including recovery of different types of corrupted data, and compared it with other algorithms. The results show that the CNN-LSTM fusion algorithm has the highest structural similarity (0.9545) and the most minor normalized mean square error (0.0016) for recovering broken video information, outperforming the methods using CNN or LSTM alone. The fusion algorithm dramatically reduces computation time and resource consumption for processing complex datasets. The combination of CNN and LSTM significantly improves the performance of broken information recovery, especially in processing video and audio data, and provides new ideas for future information processing techniques.
Exploration of grid energy efficiency influencing factors by applying principal component analysis approach
Authors: Li Jianbin , Sun Yongqiang , Zhang Li , Pan Yang 
Published: February 2024

Abstract
This paper focuses on the problem of power losses in the transmission, transformation, distribution and marketing segments of the power system, and explores how to effectively improve the energy efficiency of the power grid to optimize the cost management of the power grid. The article uses principal component analysis to accurately diagnose the energy efficiency status of the grid, and analyzes the multiple factors affecting grid losses and their degree of influence. It is found that the total loss in 10km transmission line when wind power access reaches 328kw, and the complete loss of wind power access is higher than photovoltaic access under the condition of different line lengths, which are 315kw, 321kw, and 328kw, respectively. In the use of iron transmission line, the average loss at the current of 400A is 89.600W, which is higher than using aluminum alloy material by 66.200 W. If energy-efficient aluminum alloy fittings are fully utilized in transmission lines, the average annual power saving can be up to 301,368 kWh. The study shows that it is crucial to analyze the factors affecting the energy efficiency of power grids during the planning and construction stage of the power grids.
Digital Transformation Strategy for Financial Management of Entity Enterprises in the Information Age
Authors: Huang Meng , Gao Sen 
Published: February 2024

Abstract
This study is dedicated to exploring the effectiveness of digital transformation of financial management of real enterprises in the information age. The annual reports of enterprises are collected from 2011 to 2022 by web crawler technology, and text mining algorithms, such as TF-IDF and LDA theme model, are used to establish a measurement index system of digital transformation of financial management, which is calculated by entropy weight-TOPSIS method. The results show that keywords related to digital transformation, such as “informatization” and “intelligence”, appear frequently, and the importance of the theme changes significantly in different transformation stages. During the rapid transformation period, the frequency of words such as “industrial internet” and “smart grid” exceeds 400 times. Some companies have seen significant improvements in debt servicing, operations and profitability. Digital transformation of financial management is efficacious in improving operational efficiency and profitability. Still, it is a dynamic process that requires enterprises to choose appropriate strategies based on their characteristics. The study emphasizes that enterprises should pay close attention to integrating and applying emerging digital technologies and optimize their internal governance structure to promote sustainable performance improvement.
Research on high-precision digital image correlation measurement techniques for highly stable structures
Authors: Yang Fenglong , Pan Bing 
Published: February 2024

Abstract
This study proposes a novel digital image processing system that combines a diffraction-limited resolution (DLRF)-based measurement technique with a windowed form-center tracking algorithm. To evaluate the accuracy of this system, this paper compares and analyzes the effectiveness of conventional digital image techniques and DLRF-based methods for deformation displacement measurements. In addition, the study includes thermal stability tests under ambient noise and uniform high temperature conditions to evaluate the stability performance of the system in a complex environment. The experimental results show that the DLRF-based digital image correlation method proposed in this study performs well in reducing the mean deviation (from a maximum of 5.17 × 10-3 to 1.73 × 10-3) and root-mean-square error (from a maximum of 5.14 × 10-3 to 0.75 × 10-3). It is worth noting that the DLRF method is faster in processing when using the single-precision format than the double-precision format, with a speedup of up to 1.05 times. In addition, the multiple displacement averaging processing method can effectively filter the noise in the test, and the noise effect is only in the range of 0 to 2 μm in most areas. In the analysis of test points 10-34 and 57-80, the displacement error is controlled within 5 μm, indicating that the modified structural analysis model can be used for on-orbit micrometer-scale thermal deformation analysis. The study proves the high accuracy and stability of the digital image system proposed in this paper in the measurement of deformation displacement, which provides adequate technical support for accurate measurement in related fields.
Matrix model-based Analysis of e-commerce development and urban entrepreneurial activity
Authors: Zhu Baohui 
Published: February 2024

Abstract
China’s e-commerce market size, the most active and best-developed component of the digital economy, has become an essential driver of economic growth and urban entrepreneurial activity. This paper analyzes e-commerce development and urban entrepreneurial activity through BCG matrix. It establishes the framework of AHP-SWOT hierarchical analysis model combining with SWOT matrix model and AHP hierarchical analysis. In the development of e-commerce, the national e-commerce turnover in 2020 is 37.21 trillion yuan, with a year-on-year growth rate of 57.60% in 2014. Meanwhile, in 2020, the number of e-commerce employees reaches 60 million, which is 1.23 times of the number in 2014. Due to the rapid development of the economy and e-commerce, resulting in urban entrepreneurial activity is also gradually rising, the number of private enterprises in 2012 grew from 5254870 to 18921928 in 2020, an increase of up to 260%. 2020, the number of new market entities is 27,495,700, the country’s entrepreneurial activity is a year-on-year upward trend. Through the development of e-commerce and urban entrepreneurial activity, the quality of employment and entrepreneurship in society is promoted to a higher level.
Research on the Construction of a Diversified System of Preschool Physical Education Curriculum Based on Kruskal Algorithm
Authors: Zhang Yao 
Published: February 2024

Abstract
This study constructs a diversified system of physical education curriculum suitable for young children and explores the application of personalized service method in early childhood physical education curriculum. The study designed the structure of early childhood physical education curriculum based on the principles of science, relevance and playfulness. It used the K-means clustering method improved by Kruskal algorithm to recommend early childhood physical education resources. The influence of the curriculum resource system on early childhood physical exercise was investigated by regression analysis. The study results showed that the number of weekly workouts of young children was significantly influenced by the comprehensive developmental features of the curriculum and the diversity of workout programs, (P<0.05). In contrast, the duration of consecutive workouts was mainly influenced by the comprehensive developmental features of the curriculum structural setup and the holistic nature of the curriculum goal setup (0.01<P<0.05). In addition, the cumulative duration of weekly exercise of young children was significantly influenced by regularity perception, comprehensive development and holistic goals in the curriculum resource system (P<0.005). The effectiveness of the personalized service approach in enhancing the efficacy of physical exercise in young children was demonstrated, and it was emphasized that the diversification and comprehensiveness of curriculum content should be emphasized in early childhood physical education.
Pattern recognition study of musical rhythm perception based on multimodal deep learning
Authors: Bai Wen 
Published: February 2024

Abstract
Rhythm perception is becoming more and more important in the field of music information processing and music understanding. The study first adopts signal processing methods to extract musical features, then uses feature fusion techniques to integrate features of different modalities into a single feature vector. Based on this model, the study identifies the rhythmic activation function of music and combines it with the hidden Markov model to infer the rhythm of the music. One of the key points of the study is to perform rhythm recognition on music containing drums, to explore the recognition effect. One of the focuses of the study is to recognize the rhythm of music containing drums to explore the recognition effect.In addition, the study also analyzes the Softmax output values of the music and compares the recognition effect of different models. The results show that the rhythm recognition of music using the multimodal deep learning method performs the best in terms of the F-Measure value, the Cemgil value, the Goto value, and the P-score value, with the respective 65.65%, 66.76%, 36.75%, and 36.75%. 66.76%, 36.75%, and 75.68%.Especially in the drum music recognition, the position of each drum music is accurately recognized, proving the model’s effectiveness in this paper. The research provides a new feasible method for the recognition and understanding of music rhythms and a valuable reference for the research in this field.
Enhancement Strategies of Traditional Cultural Inheritance on Moral Education in the Internet Era
Authors: Sun Danling , Zheng Chuan 
Published: February 2024

Abstract
Combining traditional culture and moral education is particularly important in the Internet era. This study explores the application of conventional culture in moral education and proposes practical strategies for integrating traditional culture into moral education. The study first analyzes the symbiotic pathway between traditional culture and moral education, emphasizing the importance of conventional culture in ecological moral education. Then, by constructing a coupled evaluation index system of conventional culture and moral education and applying the entropy value method and the coupled coordination degree model, it comprehensively evaluated the moral education subsystem in a province from 2008 to 2022. The results show that the province’s honest education subsystem scores improve from 0.1146 to 0.8133, and the coupling coordination degree shows an upward trend. Based on these findings, this study proposes strategies for integrating excellent traditional culture into moral education, including establishing a scientific concept of integration, enhancing teachers, and enriching extracurricular activities, aiming to improve the effectiveness and usefulness of moral education.
The Path of Enhancement of Traditional Cultural Heritage on Moral Education Based on ADDIE Model
Authors: Zhang Wenyan 
Published: February 2024

Abstract
With the development of society, the importance of Chinese excellent traditional culture to the moral education of college students is becoming more and more prominent. This study explores the application of conventional culture in moral education through the ADDIE model, aiming to improve the quality and effectiveness of moral education. Through the five stages of analysis, Design, development, implementation, and evaluation, the study constructed a moral education effectiveness evaluation index system. The study found that integrating Chinese excellent traditional culture into moral education can effectively enhance college students’ self-confidence, national responsibility and national pride. The empirical survey shows that in the universities adopting this model, the overall satisfaction of college students regarding the effectiveness of moral education reaches 77.90%, and the dissatisfaction is only 1.16%. The comprehensive evaluation score is 4.5731 points, showing a sound effect of moral education. The study’s conclusions that the ADDIE model has asignificantly affects It provides a new perspective and practical path for moral education in colleges and universities.
Compositional Development of Chinese Pipa Music in the New Media Era
Authors: Wang Xinjie 
Published: February 2024

Abstract
In the new media era, the creation and development of Chinese pipa music faces new challenges and opportunities. This paper analyses the characteristics of pipa music and explores its innovative path and development trend in the contemporary environment through technical means. Music signal preprocessing, feature extraction and other methods are adopted to provide an in-depth characterization of pipa music. In the study, the pipa music was classified concerning pitch, loudness, timbre and rhythm, and the musical features were extracted using techniques such as MFCC and LPC. The experimental data contained 300 pieces of pipa music of different styles, and a series of specific numerical results were derived from the Characterization of this music. For example, in the MFCC feature retrieval experiment, the retrieval accuracy of a 30-second music clip was improved by 0.024 compared to a 10-second clip. In addition, by analyzing the timbre features of 12 pipas, and using MATLAB to process the data, a quantitative judgement of the goodness of the timbre was derived. The possibility of combining pipa music with electronic music was explored with new media technology. The artistic expression of pipa music can be effectively analyzed and enhanced through scientific methods, providing fresh ideas for the innovative development of pipa music.
Discourse Practices and Identity Remodeling of Hosts on Live Streaming Platforms under the Background of Informatization
Authors: Wang Xue 
Published: February 2024

Abstract
In the age of informationization, webcasting has gradually become an essential form of media. On the live broadcasting platform, hosts transmit information through discourse practice and continuously reshape their identities, a phenomenon of great significance in both academic and practical fields. Qualitative and quantitative methods analyze the host’s discourse characteristics and identity construction process. It is found that the moderator’s discourse patterns are mainly affectionate, humorous, simple and commonplace, and the role of discourse practice in the construction of identity is revealed through Foucault’s discourse theory and the SMCR communication model. The empirical analysis shows that the hosts’ discourse themes in beauty and fashion account for up to 20% of the total, and enhance the communication effect through multimodal discourse (visual and auditory). The study also found that hosts reshape their identities through live discourse, such as transforming traditional TV anchors into online KOLs, to portray a pro-people and professional image. Hosts realize effective identity reshaping on live broadcast platforms through discourse practice, profoundly impacting emerging marketing models such as live bandwagon.