bluuebunny/crossref_metadata_2025 huggingface dataset:
Teaching Pupils to Design Comparative Experiments with Web-Based Inquiry Unit During COVID-19 Pandemic: A Pilot Study
Authors: Chao Qing , Lu Tong , Zhao Xiaoyu , Peng Qingqing , Zhao Guoqing
Published: June 2021

The COVID-19 pandemic caused many students away from the classroom. Its affecting region was so large and the inquiry learning had to move to online from offline. Although many studies had investigated the effectiveness of web-based inquiry learning, few of them conducted that under the pandemic. The pandemic took many new characters into education, such as the demand for the Internet. Hence, we conducted the pre-posttest quasi-experiment to investigate the effectiveness of online science inquiry during the pandemic. Under the instruction of teachers online, 30 fifth-grade students (19 males and 11 females) in a Chinese city completed a web-based inquiry learning program in the Web-based Inquiry Science Environment (WISE) platform. The experimental design ability test (EDAT) was conducted before and after web-based inquiry learning as the pre-test and post-test. The students’ attitude to web-based inquiry learning was also measured. The results showed, different from the studies before, the students’ score on experimental design ability decreased after web-based inquiry learning, especially in Asking Questions and Making Hypotheses subscales of EDAT significantly. No significant gender difference was detected. The students showed not a high attitude toward web-based inquiry learning. The possible factors causing that results and implications were discussed.
Multi-UAV cooperative Route planning based on decision variables and improved genetic algorithm
Authors: Zhang Jie , Li Ningzhou , Zhang Danyu , Wei Xiaojuan , Zhang Xiaojuan
Published: June 2021

In order to reduce the threat cost of multi-UAV coordination, improve the effective flight time and the mission success rate, a path planning model was designed for the effectiveness and real-time performance of multi-UAV track coordination. In this paper, based on the path planning model under the condition of non-interference, the model of minimum cost flight path of multi-UAV based on time synergy is given, and a time-sequence-space coordination model is proposed to meet the requirements of electronic jamming, mission confusion and radar illumination. The decision variables of range and threat are introduced into the collaborative function of track cost, and by adjusting the cost functions and collaborative variables in real time, the path planning model was optimized for unmanned support aircraft in coordination with multiple unmanned aircraft mission, at the same time, the genetic algorithm is improved by combining the adaptive crossover rate and mutation rate, and the optimization of track collaborative solution process is realized. Finally, the effectiveness of track coordination model of multi-UAV based on decision variables and improved genetic algorithm is verified by semi-physical simulation.
Research on HR management based on RNN expectation theory
Authors: Fang Yayue
Published: June 2021

RNN convolutional neural network is an important part of deep learning algorithm. Compared with fully connected neural network, the biggest difference is that the hidden units are not independent of each other. Not only the hidden layer neurons are irrelated to each other, but also the state of the current hidden layer cells is affected by the historical input data. This feature enables it to extract the temporal relationship of temporal data structure. One of the current outputs of one sequence is also related to the previous output. The specific expression is that the network will memorize the information before and apply it to the calculation of current output, the nodes between hidden layers are no longer connected but connected. Moreover, the input of hidden layer includes not only the output of input layer but also the output of hidden layer at the last time. RNN neural network is widely used in natural language processing (NLP): machine translation, speech recognition, image description generation, text similarity calculation. The DAFDC-RNN model in this paper is verified by the financial and real estate industry data from 2019 to 2020, which shows that the model has excellent stability and sensitivity. Compared with CNN model, it increases by 34.3% and 24.9%. The advantages of data processing as follow: it can process a large number of time series data, the processing capacity of data set is large, it can process 1000 + data, and it has more data base for time series prediction. The disadvantage as follow: Compared with CNN model, the working time of CNN model is increased by 48%, but the model has strong stability and adaptability. Taking the data from 2019 to 2020 as the input of DAFDC-RNN time series forecasting model, the salary forecast of real estate and financial industry in 2021 is obtained. Based on this data, the industry expectation of Finance and real estate industry under human resource management under expectation theory is reduced.
Effect of Chinese industrial structure upgrading on household consumption structure: evidence from CHIP data
Authors: Wang Liping , Huang Lu
Published: June 2021

Based on the data of Chinese Household Income Project (CHIP) and 117 cities in 2007 and 2013, this paper analyzed the effect of industrial structure upgrading on household consumption structure by using the ordinary least square method and quantile regression method. The following conclusions were drawn. Firstly, the upgrading of industrial structure can significantly promote the upgrading of household consumption structure. Secondly, compared with the low-level consumption structure households, the industrial structure upgrading has a stronger impact on households with high-level consumption structure. With the increase of quantile level, the influence of industrial structure upgrading on the upgrading of residents' consumption structure can gradually increase. Therefore, the government should actively promote the supply-side reform and the upgrading of industrial structure, which would be conducive to the upgrading of consumption structure.
Research on master data recognition of TCM based on analytic hierarchy process
Authors: Sang Yuhui , Tian Shuanggui
Published: June 2021

With the rapid development of information technology in the field of medical and health, the requirement for information sharing was higher and higher. TCM plays an irreplaceable role in promoting health development. The information of TCM was represented by the construction, application and data sharing of information system, and faced complicated problems. Data environment, standardized data management requirements and data standards were very important. In the field of TCM, the main line was about "TCM information integration standardization, TCM standards into information", while the main data was taken as the basis of data standardization. This paper through the investigation and summary of the existing information system and data items, according to theoretical research of main data recognition by scholars, discussed the main data identification method by using analytic hierarchy process, and extracted the main data, so as to improve quality of data management and optimize data management environment.
Efficiency optimization strategy of LLC resonant converter based on Hybrid PWM and PFM digital control mode
Authors: Xun Hehui , Shen Hanxin , Chen Wenxiang
Published: June 2021

There is a problem that the overall efficiency of LLC resonant converter decreases due to large switching loss under light load, in order to solve it, an efficiency optimization strategy based on hybrid PWM and PFM digital control mode was proposed to improve the efficiency of LLC Resonant Converter in the full load range. In order to adapt to different loads, Boost LLC converter circuit topology was proposed. The former Boost circuit was controlled by pulse width modulation (PWM), and the latter LLC was controlled by pulse frequency modulation (PFM). Pulse width modulation (PWM) control of the front Boost circuit can improve the bus voltage of the rear LLC circuit and adapt to different loads. Under the pulse frequency modulation (PFM), the efficiency of the rear LLC resonant converter under light load can be further improved by adjusting the resonant frequency. The experimental results showed that the proposed digital control efficiency optimization method could enable the Boost LLC converter to achieve high efficiency in the whole load range, and the overall conversion efficiency was not less than 94%.
Study on open operation and compactness based on constructing rough set L-f topological space
Authors: Zhong Mantian
Published: June 2021

How to construct topological space in rough set and study its topological space open operation and compactness was presented. The rough set topological space was constructed by using the equivalence class and the rough set L – f topological space was constructed by using mapping, which is first established. To obtain the essential attributes of the constructed rough set L – f topological space, the following five techniques were used: the opening operation of the L – f topological space can be defined by using the inductive method, and it was proved that the L – f topological space by using deductive reasoning which satisfied the β – open operation, the correctness and open coverage of the topological space can be defined by using the set, and it was proved that the L – f topological space was β – correctness by using deductive reasoning at the same time. Furthermore, it was verified that the constructed rough set L – f topological space by using data was very effective. The theoretical results had important significance in the promote study the topological space of rough set.
Study on gamma spectrum wavelet noise reduction based on scintillation detector
Authors: Yu Yingchi , Ge Liangquan , Li Fei , Hu Chuanhao
Published: June 2021

Wavelet transform is a time-scale signal analysis method, which holds the characteristics of the same window size, arbitrary shape change and multi-resolution analysis. Most applications of wavelet transform need to be based on signal decomposition and reconstruction, that is, through the combination of low-pass and high-pass filters to achieve frequency division, the signal is decomposed into signal detail components and large-scale approximate components. In the measurement process of the original energy spectrum data, the denoting process plays a role in reducing the incidence of statistical fluctuations to a certain extent, which is of profound significance to improve the data accuracy. In this paper, wavelet transform is used as the theoretical basis to denoise the actual energy spectrum, at the same time based on the MATLAB platform, simulation of signal with noise, using the different parameters on the signal processing. Firstly, the signal-to-noise ratio (SNR) of the hard and soft thresholds to the signal processing under the same conditions was compared, and then the change of the SNR was observed when the number of decomposition layers was changed under the wavelet basis of db3 (soft threshold). The experimental results show that: 1) under the same conditions, the soft threshold method has higher SNR than hard threshold; 2) The number of decomposition layers and the elimination moment affect the signal-to-noise ratio and smoothness of the processed signal respectively, and when the number of decomposition layers is 7-8 layers, the signal-to-noise ratio is about 29, the signal processing effect is the best. This study has shown that Wavelet method is an effective way to process energy spectrum.
Analyzing agricultural ecological efficiency in Weihai City based on the SBM, STIRPAT and SLM models
Authors: Zhang Xiang , Zheng Xungang
Published: June 2021

The essay is to study the development of ecological agriculture in Weihai city. The super slack-based measurement (SBM) model was adopted to measure the agricultural ecological efficiency of Weihai City’s seven districts and counties from 2008 to 2018. Based on the stochastic impacts obtained by conducting a regression on the STIRPAT model, the spatial lag model (SLM) was applied to empirically analyze the factors influencing agricultural ecological efficiency and the corresponding mechanisms. Our findings indicate that, in their direct effects, the population density, proportion of the primary industry, urbanization rate, agricultural mechanization rate, and agricultural development scale were significantly positive. Meanwhile, the rural labor transfer, agricultural output value per capita, and industrialization rate were significantly negative, and the planting structure was not significant. Under the spatial spillover effects, the rural labor transfer and agricultural output value per capita were significantly positive. The proportion of the primary industry and agricultural development scale were significantly negative, while the population density, urbanization rate, industrialization rate, agricultural mechanization rate, and planting structure were less significant. The suggestions put forward are to increase the level of scale, promote research and development, and adjust the industrial structure. The innovation and contribution of this paper lies in the application of the STIRPAT and SLM models to study the issue of agricultural ecological efficiency.
AGV vehicle differential drive model
Authors: Yao Yi , Sun Ye
Published: June 2021

Based on the establishment and analysis of the position model and forward kinematics model of the AGV vehicle, this paper derives the motion trajectory and related motion parameters of the fixed standard wheel and the manipulated standard wheel, and analyzes the motion of the AGV vehicle based on the specific motion parameters, to obtain the constraint sum of the motion and rolling and sliding of the differential AGV vehicle, and the relationship between the wheel speed of the AGV vehicle.
Colliding bodies algorithm with adaptive parameter adjustment strategy
Authors: Yu Chun , Liu Hui , Liu Bing , Li Yong , Fan Jian
Published: June 2021

Colliding Bodies Optimization (CBO) is a new meta-heuristic algorithm that uses collisions between objects to move to a better position so that the solution tends to be an optimal solution. Aiming at the shortcomings of CBO algorithm's poor optimization accuracy and prone to evolutionary stagnation in the later stage of iterations, this paper proposes a new Colliding Bodies Optimization algorithm with adaptive adjustment strategy. Firstly, a set of initial solutions of colliding bodies are generated by the good-point set strategy to improve the stability of the algorithm solution. Secondly, we adjust the linear strategy of the control parameters to a nonlinear strategy and combine it with the fitness value to form an adaptive parameter adjustment strategy. Finally, in order to make the algorithm escape from the local optimal, a mutation operation is performed on the position of the object. In order to test the effectiveness of the proposed algorithm, experiments were conducted on 23 benchmark functions with it and the comparison algorithm. The experimental results show that the performance of the proposed algorithm is better than other experimental algorithms.
Numerical simulation research on crack propagation of plane steel
Authors: Zhou Weidong , Sun Ye
Published: June 2021

There are still two deficiencies in the study of crack propagation in plane steel. One is that there are few studies on the theory of crack cracking and the cracking criterion from the strain field at the crack tip; the other is that there are few studies using the block discrete element method to explore the crack propagation. In view of the above two points, this paper proposes a strain strength criterion to explain the extension of tensile cracks and shear cracks in steel. This strain strength criterion assumes: (1) Tensile cracks grow along the direction of the maximum principal strain. When the principal strainγreaches a critical value, the tensile cracks begin to grow; (2) Shear cracks grow in the direction of the most dangerous stress state. When the Mohr circle exceeds the Mohr Coulomb failure line, the shear crack starts to expand. In addition, this paper applies the strain strength criterion to the block discrete element method to simulate the macro-mechanical response characteristics of steel under load and the propagation and evolution process of cracks. It is shown (1) the stress-strain curve appears elastic, stagnant, fluctuating, and falling back, etc. The basic law, and (2) the characteristics of stable and unstable growth of cracks during the whole loading process.
Evaluation and empirical analysis of the influence of disciplinary competition on innovation practice ability
Authors: Liu Zhang , Zhao Yiheng , Fang Guiying , Huang Yong
Published: June 2021

Mathematical modelling is an embodiment of the application of mathematics in various fields. Exploring the effect of mathematical modeling on the innovative practical ability of college students is conducive to college students’ understanding of mathematical modeling competitions and colleges’ education on mathematical modeling courses. It can also stimulate the confidence of students majoring in mathematics in their majors. On the basis of clarifying the theoretical basis and basic principles of the evaluation of innovative practical ability, this paper combines the characteristics of current college students to construct a college student’s innovation that includes 5 first-level indicators and 15 second-level indicators. Practice system. By the Delphi method and improved analytic hierarchy process we can determine the index weights and the weighted average model of fuzzy mathematics to analyze the collected evaluation membership matrix. We also obtain the membership matrix of the impact of mathematical modeling on the innovative practical ability of college students. Using the principle of maximum membership to get the degree of influence

V
3
*

=
0.3941

, the research results show that the mathematical modeling competition can improve the innovative and practical ability of college students.
Application of hybrid model of big data and BP network on strategy prediction
Authors: Qin Tengteng
Published: June 2021

Big data refers to the data set that cannot be captured, managed and processed by conventional software tools within a certain period of time. It is a massive, high growth rate and diversified information asset that needs new processing mode to have stronger decision-making power, insight and process optimization ability. BP neural network can be applied to the prediction of models and the study of the relationship between different models. Therefore, the combination of big data technology and BP neural network can deal with large and complex nonlinear structural problems from a statistical point of view, and with high stability and accuracy. This paper collects a cloud computing revenue from 2015 to 2019, and analyzes the impact of strategy mode value by using the combination of big data and BP neural network. The conclusion is that the correlation coefficient is 0.99762, which proves that big data combined with BP neural network can make accurate and effective decision prediction.
Construction and analysis of vocal music evaluation system based on BP-RBF-SVM integrated neural network
Authors: Jiang Nan
Published: June 2021

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Research on automatic recognition algorithm of piano music based on convolution neural network
Authors: Wu Ran
Published: June 2021

Convolutional neural network is a typical deep neural network, which combines the advantages of deep learning and image processing, improving the accuracy of feature recognition and greatly reducing the calculation of neural network. Window function is a signal with finite width in time domain. In signal processing, the use of window function can reduce the leakage of spectrum. Combining the window function and convolution neural network, the effective input in the model can be increased, and the accuracy and speed can be improved. In this paper, the music score of piano music is processed by window function as the input image of convolutional neural network. Through the analysis of the results, it is found that the algorithm has a 25.8% increase in rate compared with the traditional recognition method in rate, and the average value of F value reaches 89.24% in the accuracy test. The test results show that the piano music recognition rate of the algorithm has been greatly improved, and good results have been achieved in accuracy.
Nonlinearity analysis for cosmological inflation with minimal and non-minimal coupling of scalar field from horndeski theory for special cases: de sitter expansion and decaying scalar field
Authors: Hikmawan G , Suroso A , Zen F P
Published: June 2021

In this work, we consider De Sitter expansion and decaying scalar field in cosmological model with combination of minimal and non-minimal coupling of scalar field from Horndeski theory. As a continuity of the previous work, after the background solution and condition obtained, we continue the analysis to the nonlinearity aspect of perturbations via the spectral index of the perturbations. Spectral index of the perturbations and tensor to-scalar ratio of these special cases give sign of inflation model.
The Effect of DC Voltage on Solidification of Tetrabutyl-Ammonium Bromide (TBAB) Aqueous Solution
Authors: Rahman A , Swandi A , Anggraini R , Putri R A , Kurnia D , Sutjahja I M
Published: June 2021

This study aimed to investigate the nucleation process of 23 wt% tetra-n-butyl ammonium bromide (TBAB) aqueous solution due to DC voltage. TBAB hydrate is an example of a thermal energy storage system with a phase change temperature of around 0–12 °C. The commonly sold copper electrodes are inserted into the sample to supply the voltage continuously. The diameter of the electrodes was 1 mm, and the gap was approximately 0.5 mm. The applied voltage varied as the experimental parameter is in the range of 0 to 30 V. Two repetitive freezing-thawing experiments are performed to ensure data repeatability. The experimental results showed that the TBAB hydrate would not form until a minimum value of 20 V of DC voltage was applied. The increase of the voltage used led to higher solidification temperature and reduction of the supercooling degree.
Cosmic ray muons detection in COMET experiment group at KEK summer student program
Authors: Prihadi H L , Hikmawan G , Zen F P
Published: June 2021

In this article, we present some works on the detector part of COMET (COherent Muon to Electron Transition) experiment. The detector system of the COMET experiment uses scintillating materials and photodetectors in general. We apply high voltage input and measure the Gain of a photodetector named MPPC (Multi Pixel Photon Counter) and it is linearly dependent with the high voltage input. After that, we attach an LYSO (Lutetium-Yttrium Oxyorthosillicate) crystal as a scintillating material to the MPPC and then measure the natural radioactive process at around 597 keV energy spectrum and also detect the presence of cosmic-ray muons at around 28.4 MeV energy spectrum.
Local Existence of Classical Solutions to Scalar Field Equation on Spatially Compact Spacetime as a Background
Authors: Wijayanto Mirda P. , Akbar Fiki T. , Gunara Bobby E.
Published: June 2021

The goal of this paper is to prove the wellposedness of scalar field equation on spatially compact spacetime of Riemannian manifold. We construct the equation of motion from the Lagrangian of scalar field with non-minimal coupling, where the coupling interaction of the scalar field ϕ is proportional to the scalar curvature of the spacetime. The equation of motion has the form like non-linear wave equation. The next step is to prove local existence of solutions. We have show that both the k th linear energy and energy norm are bounded for the finite time with the initial data in H k+1 × H k. Finally, we prove the uniqueness and smoothness properties of the solution.
Rotating and twisting charged black holes with cloud of strings and quintessence
Authors: Sakti M F A R , Prihadi H L , Suroso A , Zen F P
Published: June 2021

We find a charged spherically symmetric black hole solution with the existence of a cloud of strings and quintessential matter. Then we apply the Demiański-Newman-Janis algorithm to generate the rotating and twisting counterpart. The thermodynamic properties of this black hole solution are further investigated.
Simulation of microtube irradiation on the beam tubes of GA Siwabessy reactor for in-vitro boron neutron capture therapy
Authors: Rasito  , Su’ud Z , Permana S
Published: June 2021

The BNCT in-vitro test using a microtube irradiated in the neutron beam of the RSG-GAS beam tubes has been simulated using PHITS code. The simulation was performed by modeling a microtube containing boron solution which was placed 5 cm in front of the beam tubes. Inside the beam tube was installed a beam shaping assembly (BSA) using 80 cm of MgF 2 as a filter to reduce fast neutron and gamma fluences. Microtube containing 1.5 mL of boron solution with boron enriched 10B of 90%. The microtube dose i.e alpha dose, 7Li dose, proton dose, and gamma dose were calculated for boron concentration of 1 ppm. The simulation of microtube containing 1 ppm boron solution shown that irradiated in the BSA-S5 neutron beam can produce 20 Gy dose in the shortest irradiation time of 2.5 hours.