bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental Study of Thermal Convection in a Cylindrical Layer with a Longitudinal Partition at Modulated Rotation
Authors: Vjatkin A A , Kozlov V G , Sabirov R R 
Published: June 2021

Abstract
Thermal convection of a liquid in a simply connected horizontal cylindrical layer rotating at a variable velocity is investigated experimentally. The inner boundary of the layer has a higher temperature. The cavity rotates rapidly and the liquid is stably stratified in a centrifugal force field. The parameters of the experiment correspond to the conditions when inertial waves are not excited (the modulation frequency is more than twice the cavity rotation frequency). The excitation thresholds of thermal convection and the structure of convective flows are studied depending on the rotation velocity and the parameters of the rotational vibrations of the cavity. It is found that the thermal convection is excited in a threshold way and its excitation is not associated with the manifestation of the mechanism of pendulum vibrational convection. A system of toroidal rolls with a spatial period commensurate with the layer thickness is observed in the cavity.
Carbon Coating on Surface of PVC Blood Bags
Authors: Chudinov V S , Shardakov I N 
Published: June 2021

Abstract
In this work, we studied the effect of a carbon coating created on the surface of a polyvinyl chloride blood storage bags on protein adsorption, cell culture proliferation, and on whole blood leukocytes. Based on the results obtained, it can be assumed that the preservation of leukocytes is more effective in the blood storage bags treated by the ion-plasma method, in comparison with the untreated one. The adhesion of blood cells occurs on the carbon coating of the plasma-treated surface of the blood storage bags. This can be eliminated by preliminary application of proteins that prevent cell adhesion on the walls of the blood storage bags.
Dispersion of Elastic Waves Propagating in a Fluid-Saturated Porous Medium with Cavities
Authors: Erofeev V I , Leonteva A V 
Published: June 2021

Abstract
The propagation of plane longitudinal waves in a fluid-saturated porous medium with cavities is considered in linear elastic approximation. It has been shown that as distinct from the classical porous medium (Biot’s medium) where two longitudinal dispersionless waves can propagate (a fast wave and a slow wave), in the porous-cavity media three dispersive longitudinal waves propagate and one of them is solely induced due to cavities in the material. The study of waves is carried out by obtaining and analyzing the dispersion equation, phase velocity and group velocity. The frequency spectrum density is considered, which enables to identify the degree of dispersion expressiveness. At certain values of the system parameters some regions of strong and weak dispersion, regions of normal and anomalous dispersion have been identified.
Graph Classification Method Based on Wasserstein Distance
Authors: Wu Wei , Hu Guangmin , Yu Fucai 
Published: June 2021

Abstract
Graph classification is a challenging problem, which attracts more and more attention. The key to solving this problem is based on what metric to compare graphs, that is, how to define graph similarity. Common graph classification methods include graph kernel, graph editing distance, graph embedding and so on. We introduce a new graph similarity metric, namely GRD (Geometric gRaph Distance). Our model GRD is composed of three sub-modules, which capture the differences between the graph structures from different aspects. Finally, the graph distances defined by the three modules are fused to define the similarity between graphs. Experiments show that GRD is superior to the baseline methods on the benckmark datasets.
Fake Speech Answer-Sheet Detection on Intelligent Learning App Based on Block-Based Deep Neural Network
Authors: Wu Qingzhu , Guo Haitao , Xiong Shaowei 
Published: June 2021

Abstract
In order to detect the fake speech answer-sheet obtained by text-to-speech or voice conversion algorithms on intelligent English learning app, this paper proposed the idea of block-based deep neural network (BDNN), which is constructed to extract deep features. It is composed of classification-based blocks. In BDNNs, the deep feature extracted by a preceding block serves as the input to its following block. Different from the commonly used inputs for deep features extraction, the magnitude-phase spectrum, including magnitude and phase information, is exploited to feed BDNNs in order to extract effective deep features. The experimental results show that the proposed method can detect most of fake speech answer-sheet on intelligent English learning app.
A Zero-Watermark Hybrid Algorithm for Remote Sensing Images Based on DCT and DFT
Authors: Xing SiMing , Li Tong Yi , Liang Jing 
Published: June 2021

Abstract
Remote sensing image has significant advantages such as large detection range and fast acquisition speed, so it has been used in military, environmental, meteorological and other fields. In order to improve the robustness of the digital watermarking technology on the premise that the remote sensing image information is not modified, a remote sensing image zero watermarking algorithm based on Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) is proposed in this paper. First of all, the color remote sensing image is converted into grayscale image. The transformed coefficient matrix is obtained by DFT, to the grayed host image. Secondly, in order to compress the amount of information and improve the efficiency of the algorithm, the high frequency coefficient image is transformed by DCT transform. The low frequency coefficient image which is consistent with the size of the watermark image after DCT is selected as the feature image of the host image. After that, the watermark information is scrambled by Arnold transform to improve the security of the watermark information. Finally, the zero-watermark image is obtained by XOR operation between the scrambled watermark information and the feature image. The zero-watermark image is saved in a third-party database for copyright verification. In order to verify the robustness of digital watermarking, this paper carries out sharpening, histogram equalization, Karma transform, noise, filtering and geometric attacks on the host image, and compares it with other algorithms. The NC value between the extracted watermark image and the original watermark image after attacking the host image show that the algorithm has a strong ability to resist common attacks.
Research on The Method of Grass Mouse Hole Target Detection Based on Deep Learning
Authors: Wan Jiaoxia , Jian Dongfang , Yu Dongzhao 
Published: June 2021

Abstract
Due to climate warming, increasing soil erosion and overloading of grassland grazing, grasslands around Taipusi Banner in Inner Mongolia have been degraded to varying degrees, causing widespread rodent damage. Aiming at the problem of rodent damage in the grassland surrounding Taipusi Banner, the UAV visible light image is the main research object, and the precise detection of grass mouse holes is the research goal. Computer vision technology is used to study grass mouse hole detection methods, in order to achieve rapid and accurate mouse detection. Hole function.
Comparison of Several Machine Learning Algorithms for Prediction
Authors: Song Yi 
Published: June 2021

Abstract
Currently, the analysis of bacterial drug resistance is a research hotspot in the biomedical field. However, due to the long culture experiment cycle for traditional multi-antibiotics, the tolerance degree of a specific bacterium to a specific antibiotic cannot be quickly analyzed and accurately determined. Therefore, how to analyze and predict drug resistance is still a problem to be solved. With the development of whole genome sequencing (WGS) technology, this paper proposed a variety of machine learning methods to analyze and predict the drug resistance of Staphylococcus aureus and Neisseria gonorrhoeae. First of all, the original data was preprocessed; secondly, the key features, such as information entropy and Gini index, were extracted; finally, the drug resistance of each drug was analyzed and predicted. Experimental results showed that the training of this model could achieve rapid convergence, and the recognition accuracy was as high as 97%. By comparing the experimental results of various machine learning methods, it can effectively predict bacterial drug resistance.
Three-dimensional Modeling and Performance Analysis of Permanent Magnet Synchronous Motor
Authors: Song Haixia , Song Mengmeng , Zhou Jianxin 
Published: June 2021

Abstract
The three-dimensional model of rare earth permanent magnet synchronous motor is built based on SolidWorks software, the assembly of synchronous motor and the production of explosion diagram are completed. Finally, the motor shaft model is imported into the Workbench interface ANSYS the finite element analysis software for harmonic response and modal analysis. The results show that the motor shaft model resonates under the action of 659.38hz exciting force, the displacement value of deformation is the largest in the middle of the shaft, the maximum stress appears near the keyway of the shaft end; The first four natural frequencies and modes from modal analysis. This research provides some reference value for improving the performance of synchronous motor.
Study on Test and Evaluation of Engine Mounting System Based on Transmission Force
Authors: Wang Kaiyan , Wang Lingxi , Dong Liang 
Published: June 2021

Abstract
The vibration isolation performance of the engine mounting system can be evaluated by the transmission force. The transmission force characteristics of engine mounting system are analyzed by simulation and test. The 6-DOF model of engine mounting system is established by ADAMS software. The results of modal parameters and transmission force of engine mounting system are obtained by simulation. The force sensor is made with resistance strain gauge. The sensor is calibrated by chassis dynamometer method. The transmission force of the engine mounting system is tested under the complete vehicle condition. The test results of transmission force and acceleration transmissibility are compared. It is proved that the transmission force is more suitable to evaluate the vibration isolation performance of the mounting system when the vehicle is running at medium and high speed.
Planning of Vehicle Routing with Mixed Time Windows Based on the Improved Genetic Algorithm (IGA)
Authors: Jiang Yueyang 
Published: June 2021

Abstract
In recent years, the rapid expansion of urban distribution business and the increase use of traditional fuel logistics trucks have resulted in a lot of environmental, traffic and resource problems. “Green logistics” has become the direction of urban distribution transformation, and replacing fuel vehicles with pure electric logistics vehicles has become a way to meet the requirements of urban end distribution. In this paper, the characteristics of electric logistics vehicles are combined with the constraints of cargo capacity to meet the demand for sufficient power and driving range in the distribution process. Charging operations are carried out using charging piles at fixed locations when necessary, and the time windows of customers at each node are considered and penalty factors are set so that the distribution path of electric logistics vehicles can be well planned, which makes the total logistics cost lowest.
Research on the Application and Influence of Artificial Intelligence Technology
Authors: Chen Yuzhuo , Chen Yuchao 
Published: June 2021

Abstract
At present, with the steady progress of industrialization and information technology in China, lots of high-tech, including artificial intelligence, such as financial robot and face recognition system, are gradually coming into the public view. This paper points out the constituent elements and design logic of artificial intelligence, starting from the background of the development and application of related technologies. Meanwhile, based on this artificial logic, assisted by voice recognition, image recognition, data analysis and other processes, it is applied to neural network construction, data mining, intelligent information system and other fields. Finally, taking one of the fields as an example, this article deeply analyzes the possible effect and impact of the application of these technologies, which provides a theoretical basis for the large-scale promotion of artificial intelligence technology.
Research on the Statistical Model System of Each Balanced Game Based on Big Data
Authors: Yan Li 
Published: June 2021

Abstract
The article uses the holistic, comprehensive and dynamic diversified and complex thinking mode of big data system theory. The article creates a generalized linear model for the value of each technical and tactical performance index and the game result in each score-balanced game. To define the linear relationship between the technical and tactical performance indicators of the game and the probability of winning the game; finally, adopt the data series inference method to define the significance of the linear relationship between each technical and tactical performance indicator and the probability of winning the game. The research found that the information provided by the model created in the paper can be used in football practice such as game performance evaluation, opponent information detection, selection and modification of training preparation plans.
Research on Information Fusion Technology of MEMS Gyro Array
Authors: Xue Chao , Zhang Yinqiang , Li Lijuan , Yang Shipin 
Published: June 2021

Abstract
The measurement accuracy of MEMS gyroscope is relatively low. Starting from the software level, the multi-sensor information fusion technology of MEMS gyroscope array (MGA) is used to reduce the drift of MEMS gyroscope. Firstly, a signal acquisition and processing system for communicating with ADXRS810 gyroscope based on field-programmable gate array (FPGA) is designed. Secondly, the collected drift data of the gyroscope array is preprocessed. long-term drift trend terms are obtained by ensemble empirical mode decomposition (EEMD) and the linear function fitting, and the trend terms are filtered out to obtain a smooth and normal drift signal. Then, the data fusion model based on time series analysis is built for the gyroscope array and the error analysis is performed by Allan variance. Finally, based on the AR (1) model, the moving horizon optimization Kalman filter method is used to filter the drift data of the gyroscope array. The experiment shows that when the time domain length N=3, the bias instability of gyroscope 1, 2, 3 and 4 decreases from 9.716 o/h, 8.5682 o/h, 13.484 o/h and 26.414 o/h to 1.2922 o/h, 0.61147 o/h, 1.4184 o/h and 1.6964 o/h, respectively, and the average noise coefficient decreases by more than 85%. Compared with the ordinary Kalman filter method, the bias instability has been significantly improved.
Study on an optimized grouping RAIM method
Authors: Yan Qin , Cai Chenglin , Jia Wei 
Published: June 2021

Abstract
Receiver Autonomous Integrity Monitoring (RAIM) method is widely used in the field of aviation navigation for its advantages of fast response, low cost and wide coverage. The performance of traditional RAIM is not as good as that of fault detection, and the calculation is complex. With the development of multi-constellation integrated positioning technology, the number of visible satellite is gradually increasing. Although the performance of traditional RAIM method has been improved, but it is not large, and the amount of calculation has increased greatly. In order to solve the above problems, an optimized grouping RAIM method is proposed. Through uniform grouping, fault detection can be directly used to complete fault identification, which reduces a lot of calculation and improves the efficiency and performance. The simulation results of graphics and data show that the algorithm is feasible and effective.
A Study on Recruitment of Data Analyst Based on Text Mining and Visualization Technology
Authors: Fang Fang , Zhou Yin 
Published: June 2021

Abstract
Nowadays, the demand for data analysts has increased dramatically. First the job requirements of data analyst are downloaded from online recruitment website ChinaHR.com and zhaopin.com, then the information of the recruitments are analyzed by text mining method such as word segmentation, word frequency statistics and visualization technology. At last we summarized the required skills for data analyst. We found that compared with Python, the company is inclined to use R for data analysis, the recruitment requirements of every type of company are different. Our findings can provide reference for schools and guide individual professionals to assess and advance their competencies.
Research on key technologies of edge computing in discrete manufacturing industry in the era of big data
Authors: Lu Guanghua , Liu Mingbo , Wang Zenghai , Gao Lei 
Published: June 2021

Abstract
Relying on the centralized operation feedback mode of cloud computing, the data processing capacity is improved, but the delay and energy consumption of discrete manufacturing industry are increased. A resource scheduling method for discrete manufacturing based on edge computing architecture is proposed. Based on the establishment of big data multi cluster edge cloud framework, the task priority of discrete manufacturing industry is determined. According to the task priority of each production link, the objective plan of resource scheduling and unloading is established to achieve real-time and efficient task resource scheduling. The experimental results show that the stability is higher than 78%, the real-time response rate is fast and the performance is better.
Comparison of Topsis Comprehensive Evaluation System and Computer BP Neural Network Simulation
Authors: Fan Yongjun , Liu Yijie , Xing Yunmeng , Liu Wenxuan 
Published: June 2021

Abstract
In view of the fact that the subjective determination method will be affected by natural and man-made factors, it has high uncertainty. In order to further improve the accuracy of the algorithm, considering the comprehensive effect of various factors has become the key to solving the problem. Therefore, the paper first uses the analytic hierarchy process to initially determine the weights, uses the fuzzy inference model to make preliminary predictions based on the similarity of the impact factor matrix, and then we use the Topsis-fuzzy comprehensive evaluation model to identify the impact factors, and screen out the reasonable predictors for the second time prediction. Finally, we use an example to test the effect of the fuzzy inference model based on the Topsis-fuzzy comprehensive evaluation model factor identification, and compare it with the BP model of the fuzzy optimization neural network for ice forecasting. The results show that the TOPSIS-fuzzy comprehensive evaluation model factor identification basis the above fuzzy inference model has higher prediction accuracy and better effect.
Application of Improved Neural Network Strategy in Emotion Information Discrimination
Authors: Huang Junlian , Zhao Dongyan , Lv Boxue 
Published: June 2021

Abstract
Emotional information recognition is an important application in the field of artificial intelligence. This paper analyzes the existing speech emotion recognition system, based on the improved neural network strategy to improve the accuracy of speech emotion recognition, through the combination of convolution neural network (CNN) and long-term and short-term neural network (LSTM), the emotional data information is successfully recognized, and the better accuracy is obtained.
Dependence Analysis of Insurance Businesses Based on Hierarchical Archimedean Copula Function
Authors: Tian Jing 
Published: June 2021

Abstract
The dependence between insurance businesses greatly influences the evaluation of insurance companies’ overall reserves. Because of the heterogeneity of risks, the dependence between businesses is often not exactly the same. In order to analyze the asymmetric dependence relationship, an asymmetric dependence structure based on hierarchical Archimedean Copula function is proposed. It is assumed that the loss distribution of business lines follows Log-normal, Gamma and Weibull distribution. And then a hierarchical Copula dependence structure among different businesses is established. By the empirical analysis of the historical data of CPIC’s automobile insurance, transportation insurance, enterprise property insurance and liability insurance, the optimal 4-dim hierarchical Copula structure is selected to analyze the dependence among the four. The empirical results show that automobile insurance and transportation insurance have the strongest dependence, followed by enterprise property insurance and liability insurance. At the same time, based on the obtained hierarchical structure, reserve assessment and risk measurement can be carried out, which provides an insight for analyzing the dependence among multivariate analysis.
Research on Algorithms of Fusion System of Artificial Intelligence University Curriculum Derivatives Based on Internet Big Data
Authors: Wang Yue , Sun Rui , Sun Weidong , Yu Tianbo 
Published: June 2021

Abstract
With the rapid development of artificial intelligence technology and the rapid progress of big data technology, based on the theory of artificial intelligence fusion systems based on the lives of college students, and the use of advanced technology, university courses have also expanded in multiple directions. A reasonable and scientific development model of electronic technology design. Derived by using electronic data as a carrier to optimize the derivative tools of university courses and improve performance in many aspects such as learning and sports. Using the empirical formula of the Big Data Fourier algorithm, the manual verification was verified through the statistical comparison and analysis of the competitive auxiliary review system. The application of intelligent systems in the fusion system of university curriculum derivatives and artificial intelligence algorithms based on Internet big data play an active role in the fusion system of university curriculum derivatives.
City Application Research Based on Computer Digital Information Technology
Authors: Meng Zisen 
Published: June 2021

Abstract
Advances in information technology have promoted the development of smart cities. Under the umbrella of building smart cities, the computer digital surveying and mapping geographic information industry builds an important spatial information infrastructure for smart cities by building a smart city spatio-temporal information cloud platform, and provides intelligent spatio-temporal carriers for smart cities. Based on this research background, the paper analyzes the positioning of computer digital geographic information in the construction of smart cities, and proposes the concept of geographic platform and its value in smart cities: spatial information resource sharing, ubiquitous information support and collaborative management.