bluuebunny/crossref_metadata_2025_split huggingface dataset:
Construction and analysis of vocal music evaluation system based on BP-RBF-SVM integrated neural network
Authors: Jiang Nan 
Published: June 2021

Abstract
With the development of neural network technology, integrated neural networks with better performance and stronger generalization capabilities have emerged. In order to solve the current deficiencies in the process of vocal music teaching quality evaluation in normal colleges and universities, a vocal music teaching evaluation system is proposed based on integrated neural network. The system first collects teaching evaluation data of vocal music students in normal universities. The analytic hierarchy process is used to retain important evaluation indicators, and some pre-processing of the data is used to construct evaluation learning samples; then the BP-RBF-SVM integrated neural network is used to evaluate the quality of vocal music teaching, and the evaluation results of vocal music teaching quality are obtained; finally for verification. The reliability of the model is compared with the evaluation results of a separate RBF neural network and BP neural network. The results show that the vocal music teaching quality evaluation of the integrated neural network system has a good fit and small error, which provides a new reference for the evaluation of vocal music professional teaching quality.
Regression analysis of friction resistance coefficient under different support methods of roadway based on PSO-SVM
Authors: Song Ying , Zhu Meng , Wei Ning , Deng Lijun 
Published: June 2021

Abstract
In order to solve the problem of large test workload by using the traditional mine ventilation measurement methods, the impact of support methods on the ventilation resistance coefficient was analyzed emphatically. The Particle Swarm Optimization Support Vector Machine(PSO-SVM)algorithm was used to model three types of supported roadways: wood-supported roadways, I-steel-supported roadways, and bolt-net-supported roadways. The roadway attribute parameters of the concrete supported roadway were analyzed, and the PSO-SVM regression model of the supporting parameters and the friction resistance coefficient under the specific supporting method was established, and its regression performance was analyzed. The results showed that the average relative errors of the regression results of wood-supported roadway, I-steel-supported roadway, and bolt-net-supported roadway were: -2.542%, 0.483%, and 1.605%. The friction resistance coefficient and the supporting parameters of the corresponding supporting roadway all showed a high degree of correlation. The research showed that the PSO-SVM regression model can more accurately regress the ventilation resistance coefficient and provide a new intelligent algorithm for the regression of ventilation resistance coefficient, which could have these important guiding significance for practical application.
Data mining: Application of EGARCH dynamic model on the volatility of high-frequency exchange rate data
Authors: Wang Yang-Chao , Tsai Jui-Jung , Chen Xingyu 
Published: June 2021

Abstract
Dynamic model has always been the focus of data science research. The results of dynamic models with different physical backgrounds vary greatly. Considering the time-varying characteristics and heteroskedasticity of long time exchange rate series, the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model can be used to depict the pattern of variance fluctuation and clustering over time, and the research on variance can be extended to time-varying conditional variance. In this paper, the EGARCH (Exponential GARCH) model is further adopted to analyze the high-frequency exchange rate data from 2005 to 2017, which can reduce the parameter restrictions of the GARCH model, and at the same time, the characteristics of the exponential ensure the non-negative nature of the estimated variance, so as to predict the volatility more accurately. Moreover, the different effects of positive and negative random shocks on volatility can be analyzed to fully describe the data asymmetry. Finally, we use the clustering algorithm of the EGARCH model for data mining, evaluating the influence of endogenous variables and exogenous events on the RMB exchange rate.
Auto-analysis of writing database based on corpus model
Authors: Wang Qin 
Published: June 2021

Abstract
The paper presents corpus-based writing database analysis and its application to college English writing classes. A small writing database was built by collecting 88 students’ 264 writing samples in one semester. Corpus model was used in the research for analysing connector using, cluster score and word frequency. The contrastive connector analysis reveals the difference between the collected samples and the existing Corpus SWECCL. The cluster and word frequency analysis introduces a new approach to analyse writing samples. The paper also details some classroom activities designed from research findings. Students’ in-class performance and after-class feedback both indicate that they are interested in the analysis-based classroom activities. At the beginning and the end of the semester, the study collected students’ English writing scores in the tests and adopted SPSS to assess their achievements. The paper ends with a short reflection on the enlightenments and limitations of the research.
Research on the development trend of intelligent leisure sports based on big data analysis
Authors: Liang Feng 
Published: June 2021

Abstract
Big data analysis often faces huge data samples. Its characteristics can be summarized as large amount of data, fast speed, multiple types, value, and authenticity. It is often used in data warehouse, data security, data analysis, data mining, etc. PLS regression analysis is a predictive modeling technique in big data analysis, which integrates principal component analysis, canonical correlation, and multiple linear regression algorithms. In a complex case analysis with multiple dependent variables, the main idea of components simplifies the model. The means of deriving the expressions of independent variables and dependent variables from the mapping relationship between components has a great advantage. This paper collects the attributes and market data of China's smart leisure sports equipment in 2020, establishing a PLS regression analysis model to analyze the development trend of China's smart leisure sports.
Analysis of Sanda action based on CCNN model of IBS distance measurement
Authors: Li Shan 
Published: June 2021

Abstract
IBS is a similarity measurement method of symbol sequences based on order statistics, and CCNN is a method based on CNN feature extraction and IBS distance measurement, which can construct crossover probability graph through image crossover comparison. IBS distance and Noribs distance are used to define the similarity between crossover probability graphs. For Sanda movement, it is difficult to carry out big data analysis with a small number of samples and the movement style of athletes will change greatly. In this paper, a CCNN model based on IBS distance measurement is proposed to analyze the action of Sanda. Firstly, the motion pictures obtained were de-noised and then simplified into a sequence diagram of human skeleton nodes by Delaunay triangulation. Then, 80% of the samples were input into the CCNN network for training and 20% for algorithm verification. The experiment found that in the case of a small number of samples, the accuracy of the simplified image analysis was 10% higher than that of the unsimplified image. In addition, compared with the accuracy of K-distance classification, Bayesian network classification and decision tree classification, it is found that the model can improve the accuracy by 2%-11.75% due to the small number of samples, and has higher accuracy and applicability.
Data driven sintering moisture control model based on neural network
Authors: Shi Ruijia , Cui Mengyao , Wang Yufei , Liu Jiamin , Li Zihui 
Published: June 2021

Abstract
Sintering production is an important part of ironmaking industry. The moisture content of sinter material will affect the output and quality of sinter. But at present, most sintering plants still use manual water to control the moisture content of sintering material, which leads to a great fluctuation of moisture content, and it is difficult to improve the sintering efficiency. Only stable control in the best range can stabilize the working condition of sintering machine. Therefore, this paper studies the water control scheme and designs the control system based on the existing data, and uses the established data driven moisture control model to control the appropriate amount of water, and compares and analyzes the advantages of the control ability of different models compared with manual water. Firstly, we eliminated the invalid data, found that five materials were significantly correlated with water content, and then established a multiple linear regression model to further study the influence of the proportion of different materials on water content. Secondly, we established a data-driven sintering moisture automatic control model. The neural network model is used, the input variable is the material usage, water addition and moisture measurement data, and the output variable is water addition. The prediction results obtained by simple BP neural network and NARX neural network with feedback are compared and analyzed. It is found that NARX neural network has smaller prediction mean square error, more stable prediction results. Thirdly, we compared the data-driven NARX model prediction results with the results of manually adding water to control the moisture content of sintering, and made quantitative analysis with standard deviation and coefficient of variation. It was found that the standard deviation and coefficient of variation of NARX model prediction results were smaller, so NARX neural network model was more stable and excellent. If this model can be popularized in industrial production, the output and quality of steel will be improved, the production cost will be reduced, the labor resources will be saved, and the enterprise will get more benefits.
Research on neuron PID- controlled simple wind tunnel system
Authors: Huang Jian 
Published: June 2021

Abstract
The motion of the object in the wind tunnel is irregular. In order to control it accurately, a simple device is designed. The table tennis is blown by fan, its position in wind tunnel is measured by ultrasonic module, and its motion is controlled by PID algorithm. In order to accurately control the position and orientation of table tennis in the wind tunnel, an improved adaptive PID control algorithm is used to generate PWM wave to control the wind force of the axial flow fan. The online adjustment of P, I and D parameters is realized to improve the adaptability of the system. The test results show that the table tennis can hover at any given position or move regularly through the self-adaptive PID adjustment and the automatic adjustment of online parameters.
Evaluation system of ideological of college students based on improved grey relational algorithm
Authors: Dong Li 
Published: June 2021

Abstract
Grey relational algorithm is a branch of grey system theory, which seeks the numerical relationship between each subsystem through the analysis of the correlation degree of each subsystem. This algorithm can provide a quantifiable measure of the development and change of a system, which is very suitable for dynamic process analysis. This paper takes the college students’ course performance indicators and extracurricular learning indicators in the second semester of the third year of the university and the second semester of the fourth year of the university as the analysis factors, improves the grey correlation algorithm and establishes the corresponding evaluation model of the ideological status of college students, and collects the indicators of 400 students’ Information for model fitting. Experimental verification shows that the average relative errors of the model are 3.78% and 3.26 respectively, which proves that the model has high accuracy and is very suitable for dynamic evaluation of college students’ ideological status.
The mean curvature estimate for the level sets of solutions of the Monge-Ampère equation on Riemannian manifold
Authors: Yu Xuemei 
Published: June 2021

Abstract
For the fully nonlinear elliptic Monge-Ampere equation det D
2
u = 1 with homogeneous Dirichlet boundary value condition, in this paper, a function related to the curvature of the level set of the solution was established, then the differential inequality of the strictly convex solutions of the equation on four-dimensional Riemannian manifold was got. The maximum value of the auxiliary function at the boundary was obtained by using the maximum principle and the mean curvature estimation for the level sets of the solution was given.
Analysis on equity of health human resource allocation in Hubei Province based on the Gini coefficient and a HRDI model
Authors: Xu Ziyan , Lei Xiaosheng 
Published: June 2021

Abstract
In order to fully understand the current situation of health human resources in Hubei Province, two mathematical models, Gini coefficient and health resource density index, were used to analyze the equity of health human resources in Hubei Province, and the Lorenz curve was drawn. After clarifying the distribution of four types of health technical personnel, the result showed that the fairness of distribution by population was better, and the fairness of distribution by geography area needed to be improved. It was suggested that policy makers should consider the geographical area as the factor of resource allocation, and encourage medical college graduates to work in less developed areas. Regions with rich health human resources and regions with less rich health human resources can promote the balance of health capacity through remote construction of medical consortium.
Analyzation on the current situation of informatization talent team based on big data
Authors: Xu Ziyan , Xiao Yong 
Published: June 2021

Abstract
Big data analysis has always been an important way to plan the development of talents. In recent years, due to the influence of national policies, the flow of talents has been in a high incidence state. In this paper, the big data analysis mode is adopted to analyze the data from the investigation informatization construction in 2019. Compared with the previous data, the development trend of talents is predicted, and the optimization structure and other methods are proposed. AHP is used to judge the current situation. After the big data analysis, the talent construction plan is planned according to the current situation and the methods of talent re-education is put forward to expand the current talent quality.
Research on the forecast of coal price based on LSTM with improved Adam optimizer
Authors: Liu Xinrong 
Published: June 2021

Abstract
Coal is a primary basic energy in China and the changes in coal prices will affect the production of its downstream industries. Forecasting coal prices will benefit companies' decision-making on future development. With coal price as the research object, this paper selects 6 factors that affect coal prices, and uses deep learning technology to propose an LSTM neural network model with improved Adam optimizer to forecast it. The experimental results show that the forecast performance of this model is obviously better than that of the traditional model, which not only improves the forecast accuracy, but also provides a reference for the future development and decision-making.
Evaluation and Study on influencing factors of agricultural products logistics efficiency based on DEA-Tobit model -- from panel data from 2010 to 2019
Authors: Yu Hongshen , Dai Yundi , Zhao Lihong 
Published: June 2021

Abstract
This paper is to evaluate the agricultural products logistics efficiency in China by means of the software Deap2.1 on the basis of the panel data of agricultural products logistics related indicators from 2010 to 2019; analyze the influencing factors of agricultural products logistics efficiency by means of Tobit model. The results of the study show that: It’s low of the comprehensive efficiency of agricultural products logistics, which is significantly affected by the economic development level, location advantages and the utilization efficiency of agricultural products resources. There are still deficiencies in the input of professional talents, etc. and the scale effect has not yet been realized, and the overall logistics capacity of agricultural products is weak; This paper puts forward the reasonable allocation of input resources from three levels of government, industry and logistics subjects, to improve the logistics efficiency and realize the cost reduction and efficiency increase of agricultural products.
Application of improved recursive regression algorithm on I&O model under COVID-19
Authors: Liu Yang 
Published: June 2021

Abstract
Regression algorithm is an important data prediction method of machine learning. It can establish calculation matrix through the existing data and obtain the hidden characteristics in the data and other valuable information. Due to the excellent data processing ability, regression is widely used in gray data prediction, algorithm model building, data analysis and other fields. In this paper, the multi-layer recursive regression is adopted and improved for the regression algorithm with double influence factors, which increases the processing ability of multi class data. Data of import and export trade between China and the United States in the past five years is collected to create the future prediction model when the epidemic does not occur. Meanwhile, the data of four months after the outbreak to calculate the condition of the future prediction model of the epidemic situation in COVID-19. Using two opposite conditions as different data of regression algorithm calculation, different prediction algorithm formulas are obtained. After multiple calculations, the average value increases the sensitivity of the model. The calculation results show that the improved recursive regression algorithm has higher stability and sensitivity.
Modeling and simulation of the anti-lock braking system based on MATLAB/Simulink
Authors: Cao Wen 
Published: June 2021

Abstract
The article illustrates basic operations and control features of ABS system. To Control the ABS Slip Ratio with Simulink, and create a single wheel ABS model according to the ABS principle. It produces Simulation curves, which verifies that the Auto ABS has good braking performance and direction of the manipulation.
Research on modeling of equipment supporting command automation system based on colored petri net
Authors: Zhu Qilin , Sun Ye 
Published: June 2021

Abstract
UML (Unified Modeling Language) has become the factor standard for object-oriented analysis and design modeling. The design result of the UML-based equipment support command automation system model is a static representation of the system, while the nature of the equipment support command automation system is dynamic. Colored Petri Net (Colored Petri Net, CPN) has a strong description ability, a rigorous mathematical foundation and a variety of analysis methods, and it can be run in simulation. A method for mapping from UML products to CPN (Colored Petri Nets) executable models that can be used for logic, behavior, and performance evaluation of the architecture is proposed. First, the general description of UML modeling of the equipment support command automation system is carried out. Then it elaborated the process of the equipment support command automation system mapping from UML products to CPN to build executable models.
AI based design of camshaft-free valve train driven by diesel engine
Authors: Li Qiang , Wang Yunkai 
Published: June 2021

Abstract
Aiming at ZS1105 single-cylinder diesel oil, based on AI technology, a camshaft-free valve train compared with the original engine was developed by electro-hydraulic drive. The test results show that the mechanism can improve the intake and exhaust efficiency and reduce the valve seating impact.
Black-box adversarial attacks based on random coordinate selection
Authors: Dong Kaikun , Shi Jiao , Guo Li 
Published: June 2021

Abstract
Recent studies show that deep neural networks are vulnerable to adversarial attacks, and the research of adversarial attack has become a hotspot in the field of artificial intelligence security. The decision-based black-box attack is one of the most challenging problems in the field of adversarial attack. Decision-based black-box attacks usually have the problems of high query times and low attack success rate. Several algorithms have been proposed to solve this problem and one of the algorithms presented by Cheng et al. models this attack as an optimization problem. However, this algorithm is based on the method of adding perturbation to the whole pixel, the number of queries is still very high and the convergence perturbation is not necessarily the minimum. In this paper, we present an improved strategy based on stochastic coordinate selection, hoping to improve the query efficiency while reducing the distortion. The convergence of the improved algorithm is analysed, and experiments are carried out on CIFAR-10 and ImageNet datasets. It has been shown that, compared with the original algorithm, our algorithm can find lower distortion using the same number of queries, and the success rate can be improved to different degrees under different query restrictions.
Individual factor analysis of wrestler’s performance based on SVM
Authors: Xu Naidan , Zhao Linlin , Wu Zhengzhi 
Published: June 2021

Abstract
Support vector machine (SVM) is a binary classification model, its algorithm is to map the sample data into a high-dimensional space. The hyperplane found in the high-dimensional space can accurately separate two kinds of data samples and maximize the interval. According to the problem of randomness and data imbalance of wrestlers' performance data, this paper proposes a new SVM model to analyze the personal factors of wrestlers' performance. Principal component analysis and chaos analysis are applied to optimize SVM model. Compared with BPNN, GM, ARIMA, LSSVM model, the contrast experiment result shows that while the number of samples increased to 300, the SVM model of the data processing time is less than 20 seconds. Operation time reduced minimum by 11% (compared with BPNN) and maximum by 60% (compared with GM). At the same time, the accuracy of sample analysis of SVM is about 90%-95%, which has a higher accuracy than other methods. Finally, the G-mean and F-value of the SVM model in the analysis of each principal component are significantly higher than the other four models. The G-mean value increases continuously when the imbalance is between 0.1 and 0.2, while the G-mean decreases gradually when the imbalance is greater than 0.324, indicating that the closer the decision boundary is, the closer the predicted results are to the true value.