bluuebunny/crossref_metadata_2025_split huggingface dataset:
Systematic Research on Measuring Acceleration of Gravity by Laser Interferometry
Authors: Dang Chun , Li Jing , Tang Jie 
Published: August 2021

Abstract
Gravity acceleration is an important physical quantity, and its precise measurement results have important applications in practice. According to the principle of measuring gravity acceleration in college physics experiment, a scheme of measuring gravity acceleration by laser interferometry is designed, and the main sources and sizes of experimental errors are analyzed and calculated. Compared with the common simple pendulum method and falling body method, the theoretical results show that our measurement method has higher precision and is feasible to accurately measure the acceleration of gravity.
Grey Theory and AHP Applied in Performance Evaluation of Tactical Communication Network Information System
Authors: Chen Qi , Yi Maoxiang , Man Xin 
Published: August 2021

Abstract
The tactical communication network is the core technology of digital battlefield communication. Research on the interference effectiveness of tactical communication networks can ensure that the information services of tactical communication can be transmitted in real time and reliably. The article analyzes the interference effectiveness evaluation index system of tactical communication networks, uses indicators such as route discovery time, throughput, delay, and packet loss rate to evaluate network interference effectiveness, and proposes an effectiveness evaluation method based on AHP and gray theory. Finally, the networking software OPNET is used to conduct simulation experiments to obtain the experimental data before and after the interference, so as to obtain the effective evaluation results of the interference effectiveness of the tactical communication network. The research results have theoretical guiding significance for the evaluation of the interference effectiveness.
Research on Trend Forecasting System Utilizing Big Data Network and Information Technology
Authors: Xing An , Yan Shi , Yang Zang , Yan Lu , Yue Ren 
Published: August 2021

Abstract
In this paper, we take big data, the GDP of the United States from 1992 to 2020 as the research object. Through SPSS software analysis and descriptive statistics, we can compare the GDP of Biden and Trump during their tenure. Using big data’s analytical technology, the line chart we get visually shows that the election of different candidates will shape different strategic patterns of global economic and financial development. A new comprehensive factor W is obtained from the data that directly reflect the overall GDP of the United States. Finally, by establishing a forecasting model, we can find out the comprehensive event factors that will occur due to different political positions and policy plans between Biden and Trump in the future. In the end, we use the results of big data’s analysis to show that Biden’s economic plan may increase US GDP growth by 1.2 percentage points in 2021, push GDP growth to 4.9%, and help the US economy recover the economic losses caused by the epidemic in mid-2021.
Food Supply System Analysis Method and Information System Establishment with Quantitative Analysis
Authors: Chen Yu , Zeng Yanxi , Du Jiatao 
Published: August 2021

Abstract
Because a vast global system of national and international food producers and distributors controls the lifeblood of global food, our global food system is unstable even in well-functioning parts of the world. Therefore, it is particularly important to establish a food system that takes efficiency and profitability as the main considerations and allows for relatively cheap and efficient food production and distribution. In this paper, on the basis of the existing evaluation indicators of grain system, we establish a multi-level index system through principal component analysis. After that, we integrate the index into the grain system index through entropy weight method and coefficient of variation method, and establish a comprehensive evaluation model. On the basis of the evaluation model, the index is calculated twice, and the optimization model is established combined with the multi-objective programming algorithm. The simulation results show that the two weights of energy efficiency and per capita cultivated land area account for a large proportion in the optimized system.
The Systematical Solutions About Victoria’s “Fire Emergency Drones” Quantity and Location Solutions
Authors: Niu Zhe 
Published: August 2021

Abstract
For more than a year, there have been severe fires in almost all Australian states, with the eastern state of Victoria in particular. This paper aims to address three specific problems in “fighting wildfires” with three mathematical models. On the basis of analyzing the data, Model 1 obtains the specific fire situation of more than 5 months at the end of 2019. Since the occurrence of fire follows Poisson distribution, we use Monte Carlo method to generate random numbers that meet such distribution for simulation. Finally, the entropy weight method + Topsis method was used to determine the optimal combination: 13 SSA UAVs and 19 repeater UAVs, and the cost was $242,000. Model 2 needs to consider how equipment increases under extreme fires. To get the worst fire scenario in 10 years, we used cellular automata to simulate the occurrence, spread and extinction of fires. For Model 3, We use the improved genetic algorithm, by means of crossover, combination mutation, position mutation and displacement mutation of genes are carried out to select a certain number of excellent individuals. The algorithm balances the performance and accuracy, and finally obtains that the layout of the UAV should be concentrated in the east side of Victoria State as far as possible. In addition, we also analyzed the robustness and sensitivity of the model, it turns out that the model is robust.
Information System Construction and Research on Preference of Model by Multi-Class Decision Tree Regression
Authors: Mao Zhanpeng , Zheng Tingting , Lian Zongqiang 
Published: August 2021

Abstract
Through the construction of customer asset allocation decision preference model based on machine learning, the input variables are set as demographic variables, family economic conditions, personality psychological characteristics and risk attitude, and the output variables are customer asset allocation decision preference choices. machine learning algorithms such as decision tree and support vector machine are used to predict customers’ asset allocation decision preference, and compared with traditional prediction methods. The results show that the machine learning algorithm can predict customers’ asset allocation decision preference to a certain extent, and its performance is more effective than the traditional prediction method. Combined with the sample data, this paper analyzes the impact of venture capital on the growth ability of small and medium-sized enterprises, uses entropy method to evaluate corporate growth ability and corporate governance respectively, and further establishes a regression model to verify the intermediary role of corporate governance. The structured macro-prudential monetary policy rules with different response coefficients to the leverage ratio of different enterprises can effectively improve the level of social welfare, and the combination of optimal response coefficients is different under different kinds of shocks. Venture capital involvement in small and medium-sized enterprises can significantly improve the growth ability of small and medium-sized enterprises, and the support of venture capital can help enterprises improve the level of corporate governance. The experimental results show that the scorecard model constructed by the proposed method has good stability. The more obvious the improvement of the level of social welfare caused by the structural macroprudential monetary policy rules. The accurate prediction of asset allocation decision preference helps to improve customer decision-making efficiency and satisfaction, and reduce the labor costs of financial institutions..
Research on Opinion Target Extraction and System Information Collection Analysis for Reviews
Authors: Gao JianNan , Zhao Yanyan 
Published: August 2021

Abstract
Though the study of opinion target extraction of movie review, this paper proposed a method for opinion target extraction of movie review based on deep learning, and analyzed its emotional polarity. Finally, the experimental results were displayed in a visual manner to help users judge the details of the movie content. In this paper, by establishing a bidirectional short-term and short-term memory network model, the opinion target extraction task is translated into a sequence labeling task, which improved the accuracy of opinion object extraction. This paper crawled and organized the Chinese movie review corpus from the Internet and the experimental results proved the effectiveness of the proposed method.
Enhancing Data Applied in the Research of Partial Discharge Systematic Diagnosis
Authors: Yang Yi , Bai Demeng , Liu Meng , Dou Huiling 
Published: August 2021

Abstract
The existing problems in the current partial discharge PRPS sample enhancement method have been summarized in this artcle, and a GIS UHF partial discharge data enhancement method has been proposed which is based on noise coupling, Gaussian blur and so on. It solves the problem of unable to collect a large number of defect maps and form a sample set due to the lack of GIS partial discharge cases, which limits the application of intelligent algorithms such as deep neural networks. This partial discharge PRPS sample enhancement method has been proved through application that it can effectively enhance the partial discharge sample set, make the samples evenly distributed, and meet the training needs of intelligent algorithms such as deep neural networks. The intelligent diagnosis algorithm trained using the enhanced sample set has a high diagnostic accuracy rate of 95%, which has practical application value.
Review of data scraping and data mining research
Authors: Wang Yuguang , Zhu Dengyun , Zhang Bin , Guo Qi , Wan Fucheng , Ma Ning 
Published: August 2021

Abstract
In recent years, with the advent of the era of big data, the importance of data has also become more prominent. This article introduces the characteristics of today’s Internet data from the background of big data, and the main method of data scraping-crawlers. In addition, the concepts of data mining, as well as hotspots and trends are briefly introduced. Highlights the importance of data scraping and data mining in today’s Internet field.
Realization and optimization of Chinese syntactic analyzer based on PCFG
Authors: Zhu Dengyun , Zhang Bin , Guo Qi , Wang Shuang , Wan Fucheng , Ma Ning 
Published: August 2021

Abstract
As a basic research work, syntactic analysis is also a key technology in the field of natural language processing, and is the basis for deep understanding of language. The syntactic analyzer is an important tool for syntactic analysis. The Probabilistic Context Free Grammar (PCFG) model is the basic model of most parsers with excellent performance. Good effect. This article integrates named entity clues into PCFG-based Chinese syntax analysis models, completes joint learning and decoding of named entity recognition, syntactic analysis, and implements a fast and reliable decoding parser.
Simulation of non-motor vehicle and pedestrian mixed crossing behavior
Authors: Zhang Yi , Ge Jing 
Published: August 2021

Abstract
In order to study the mixed traffic of non-motor vehicles and pedestrians at urban intersections, a two-dimensional cellular automata model was established by analyzing the movement characteristics of non-motor vehicles and pedestrians, which included the field, cellular space, attributes, and forward decision-making of pedestrians and non-motor vehicles. This model can reflect the interweaving and interference behavior of non-motor vehicles and pedestrians in the time window of the intersection, and describe the behavior characteristics of non-motor vehicles and pedestrians in different stages of crossing the intersection.
An improved denoising model for convolutional neural network
Authors: Wang Wenjing 
Published: August 2021

Abstract
In recent years, computer vision and many other research fields have put forward higher and higher requirements for image quality, and image denoising has become an important research direction. Aiming at the problems of general image denoising effect and long algorithm convergence time, this paper makes an improvement on the original network structure of denoising convolutional neural network DnCNN model and adds channel attention mechanism in the penultimate layer. The channel attention mechanism further improves the signal-to-noise ratio (SNR) and optimizes the noise reduction effect through convolution and deconvolution neural networks. The experimental results show that, under the same noise reduction level of 25, the PSNR of the proposed DnCNN2 algorithm is 1.25dB and 1.62dB higher than that of the DnCNN algorithm after the verification of the BSD68 and SET12 test data sets. Structural similarity (SSLM) was increased by 0.0011 and 0.0103, respectively. Meanwhile, the convergence time of the algorithm becomes shorter. Compared with traditional deep learning image denoising algorithms, the noise reduction effect and operation efficiency of the proposed method are also improved, which has certain advantages.
Design of Risk Early Warning Model Based on Big Data
Authors: Shen Qi 
Published: August 2021

Abstract
With the development of China’s economy, pollution has become more and more serious, so total pollution control has become a priority for governments at all levels. The center of total pollutant emission reduction is to formulate scientific and reasonable pollutant emission reduction policies based on the forecast of pollutant emission in the next few years. According to the national requirements, the emissions of four pollutants, namely sulfur dioxide, nitrogen oxide, ammonia nitrogen and chemical oxygen demand, need to be counted. The research objective of this paper is to establish a combined model applicable to the emission prediction of sulfur dioxide, nitrogen oxide, ammonia nitrogen and chemical oxygen demand by analyzing the impact of population factors and industrial distribution on pollutant emissions and combining the grey system theory and neural network theory. According to the characteristics of pollutant discharge in a certain city, a pollutant discharge prediction model based on GM (1,1), GM (0,4) and BP neural network is proposed.
Design and Implementation of English Online Examination System Based on QT
Authors: Zhuo Wang 
Published: August 2021

Abstract
With the development of society, all fields will basically involve examinations. Nowadays, most of the papers in the form of paper media are used for examinations. The examination of paper media is heavily dependent on wasting natural resources and is difficult to improve effectiveness. For these reasons, the extensive use of test software is essential. The design and implementation of the text is based on the C/S mode exam management system. The system sets different permissions for each user category. People of different privileges use different functions. The system displays all the functions of the examination on the client, realizes the difference of information sharing and processing in real time, reduces the influence of human factors on the fairness of the examination, and greatly improves the efficiency of the examination.
A Virtual Modeling Method of Digital Media Image Synchronization Based on Motion Hybrid Algorithm
Authors: Yang Yanyan , Wang Zhiping , Yang Caixia , Zhu Huijun 
Published: August 2021

Abstract
In virtual reality digital media, motion capture system is used to build basic motion library. Then the basic motion is processed by motion editing technology. Motion blending is one of the most practical and complex editing techniques. This paper proposes a real-time synchronization algorithm based on motion blending, so as to better mix motion dynamically, avoid unexpected effects, and create complex virtual digital media animation. This paper adopts the strategy of data and model hybrid drive. The motion generation and control technology of virtual human is studied from the following aspects: Modeling and simulation method of motion system, grasping of virtual human considering the change of whole body posture, fast generation of operation action, automatic interactive generation of virtual human’s key frame posture, multi priority editing synthesis and interactive control of virtual human’s whole body motion. In this paper, the corresponding control strategy and model are proposed, and the traditional algorithm is improved. Experimental results show that the method improves the efficiency and accuracy of digital media generation. This method provides a reference for the research of digital media image synchronous virtual modeling method based on motion hybrid algorithm.
Design of Library Mobile User Behavior Analysis model for Personalized Information Service
Authors: Tang Miaoji 
Published: August 2021

Abstract
This paper summarizes the research status of Library mobile user behavior at home and abroad, and provides a reference for the design of Library mobile user behavior analysis model for personalized information service. Using the method of theoretical research and model analysis, the relationship between user behavior sequence and user behavior characteristics is studied from three aspects of data collection, data processing and data application. This paper defines and represents the invisible feedback behavior of Library mobile users on IOS app, android app and web page and product UI by combining the front-end operation behavior with the back-end business database, collects the data from three aspects: basic information field, user behavior related field and configuration flume of mobile users’ client, and introduces the time dimension, user dimension, Department dimension, resource dimension, resource dimension The data warehouse level modeling is carried out from six dimensions of behavior dimension and entrance channel, and the data application is realized from two steps of user behavior sequence prediction and user interest guidance.
Analysis of Big Data Embedded Mapping Model Oriented to Prediction of Subordinate Relationship
Authors: Xu Hui 
Published: August 2021

Abstract
With the rapid development of information technology, research on big data information has gone deep into all walks of life, and massive amounts of data spew out anytime and anywhere. This paper proposes a domain-subordinate relationship acquisition that combines multiple strategies and word representations. From semi-structured text and unstructured text, the candidate entity upper-lower relationship entity pairs are extracted, and then the obtained candidate upper-lower relationship entity pairs are verified by the support vector machine method to obtain high-quality candidate entity pairs. In the training phase, an exploratory method is designed to search for the optimal solution, and a greedy mechanism is introduced to evaluate the effectiveness of the reinforcement learning agent, so that the virtual node mapping scheme can continuously move towards higher system returns. Evolution has finally achieved the optimal virtual network mapping decision, so that large-scale tasks can be deployed to task processing nodes in the appropriate underlying network to achieve efficient task execution in a big data environment. The experimental results show that the Tard method can effectively avoid model overfitting and improve the task recognition processing ability in the actual application process under the premise of meeting large-scale task requests.
Design of 3D Scene Visual Communication Modeling Based on Virtual Reality Graphics Rendering Framework
Authors: Zheng Jintao , Liu Qian 
Published: August 2021

Abstract
Virtual reality is a kind of computer simulation system which can create virtual environment. People can interact with the three-dimensional environment generated by computer. In this paper, the real-time computer graphics technology, three-dimensional modeling technology and binocular stereo vision technology in virtual reality technology are studied, and the binocular stereo vision optical system is designed. This paper studies the visual communication modeling design of 3D scene under the framework of virtual reality graphics rendering. In this paper, we design and make a three-dimensional model, and develop a virtual indoor scene application. In this paper, the 3D model is used to build the scene, and several commonly used rendering technologies such as texture mapping, material, lighting calculation, transparent effect and shadow calculation in unity engine are used to render the scene. The experimental results show that this method can improve the design efficiency and the accuracy of design matching.
Research on Addressing Technology of Internet of Things Unique Resources Based on RFID
Authors: Huo Zhenxuan , Liu Tianjun , Li Tiannian , Zhong Hui 
Published: August 2021

Abstract
The global nature of the Internet of things obviously has the problem of cross domain communication, so the Internet of things also needs a set of perfect resource addressing technology to meet its resource addressing needs and promote the interconnection of the Internet of things. Through the analysis of five key elements of resource addressing, such as resource name, resource address, addressing mechanism, update mechanism and security mechanism, this paper summarizes the resource addressing characteristics of Internet of things, which is not only conducive to the design of resource addressing model of Internet of things that meets the demand of resource addressing, but also the necessary premise to solve the key technology of resource addressing of Internet of things. In order to solve the address conflict and addressing problem between different standards of radio frequency identification (RFID), a hierarchical addressing model based on RFID resource name space is proposed. This paper analyzes the characteristics of RFID resource addressing and the existing RFID addressing technology, finds out the defects of addressing technology based on domain name system, introduces the formal expression method of RFID resource namespace, and designs a hierarchical addressing model.
Design of IoT Sensor Terminal Based on RFID and Wi-Fi Technology
Authors: Li Meilian 
Published: August 2021

Abstract
The Internet of Things has penetrated into all aspects of human production and life. This article has developed an Internet of Things sensing terminal based on RFID and Wi-Fi technology. This article uses the information management function provided by RFID electronic tags and the convenient networking function provided by Wi-Fi to design. We introduced the system performance requirement analysis and system architecture design. Starting from the selection of the S5PV210 processor and each module, as well as the communication interface between each module and the processor, we design the overall terminal hardware architecture. According to the Android architecture, the porting and development of related hardware module drivers, the design of intermediate programs, and the design of the JNI communication interface of the information acquisition module are introduced. The design of the Android upper-level UI program of each module and the PC upper computer GUI program and the performance test of each information collection module have been completed.