bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis on influencing factors of X-shape dress silhouette based on eye-tracking technology
Authors: Nie Qiu Xia , Li Yue , Wang Xiao Gang 
Published: April 2021

Abstract
For a long time, the research of inteligent garment pattern making has become a difficulty in the research of garment science, which involves the summary of the rules of garment pattern making and the accurate control of the shape of garment. In this paper, eye-tracking technology is adopted to analyze the influencing of factors of the silhouette of X-shape dress. Multivariate analysis of variance and average of participants’ eye-tracking were conducted for different design styles and interest areas of the X-shape dress by using the five interest area’s Fixation Duration, Visit Count and other indicators. According to the research, the influencing factors of X-shape dress silhouette are waist, chest, hem, hip and shoulder. Therefore, it is of great significance to take waist measurement, chest measurement and hem as the main factors affecting the X-shape dress silhouette, and to further study of the relationship between the structure and X-shape dress silhouette, so as to realize intelligent rapid pattern making based on computer technology.
Research on intelligent Diagnosis method of Electric vehicle Charging Fault based on artificial intelligence expert system
Authors: Yang Lutong , Gao Hui , Duan Haowei 
Published: April 2021

Abstract
To realize charging electric vehicle safety, this paper studies the latest artificial intelligence learning algorithm, combined with the characteristics in the process of electric vehicle charging, based on the deep learning algorithm of charge-discharge failure correlation method, combining with the characteristics of the expert system, design of electric vehicle charging process of the fault intelligent diagnosis system structure, characteristic information database, knowledge base, reasoning machine, and for battery, charging pile, power supply equipment failure correlation between the integration of online diagnosis, which can effectively improve the electric car battery safety, promote the healthy development of electric vehicles.
Applying computed tomography angiography in diagnosing and classifying abdominal aortic aneurysm
Authors: Yuan Biying , Liu Jianhua , Tian Jiale , Xu Nannan 
Published: April 2021

Abstract
Abdominal aortic aneurysm (AAA) is the most common arterial dilatation disease. Computed tomographic angiography has been used all the time in the diagnosis of AAA. Applying the techniques in AAA, we can not only locate the position of AAA, but also classify it, and provide more valuable information for clinicians. Currently, post-processing methods include MPR, CPR, VR. These methods have their own advantages.
An Active Disturbance Rejection Control Design Method Based on Disturbance Separation
Authors: Zhang Yuejie , Zhang Peng , Ran Chengping , Zhang Xinyong 
Published: April 2021

Abstract
In order to further improve the stability accuracy and anti-disturbance ability of the photoelectric stabilization platform, this paper proposes disturbance separation active disturbance rejection (DSADRC) method. Disturbance separation active disturbance rejection control makes full use of the available system model information and uses disturbance separation to separate the known disturbances from the total disturbance to be estimated, and improve the estimation accuracy of expanded state observer (ESO) by reducing the load. At the same time, it gets rid of the constraints of the standard integral series type in the traditional active disturbance rejection control, and takes the actual theoretical model of the system as the compensated model of the system, thus realizing the reuse of the system controller and reducing the workload of control algorithm design. And through simulation experiments, the DSADRC method is compared with the commonly used expected frequency characteristic method (EF), linear active disturbance rejection control (LADRC), model-assisted linear active disturbance rejection (MLADRC), and the experimental results show that: The DSADRC has better dynamic response performance and disturbance suppression ability.
Research on human-vehicle interaction based on natural language processing
Authors: hu Wei 
Published: April 2021

Abstract
With the rapid development of intelligent networked vehicles and driverless technology, the importance of the dialogue between human and vehicle artificial intelligence has also become prominent. In the case of the driver outputting speech and converting it into text, how to correctly distinguish the driver’s intention and make the correct response is the purpose of the research. Google released the first-generation BERT (Bidirectional Encoder Representations from Transformers) training model in 2018, bringing natural language processing methods to a new way of understanding. This article analyzes the current mainstream machine learning algorithms and applies them to natural language processing (NLP) to classify text data sets. The purpose is to study various machine learning models to achieve higher accuracy of text classification. Research results not only lay the foundation for improving vehicle driver interaction, but also serve as a reference for the natural language processing part of the future car networking and driverless technology.
Research on Fault Diagnosis and Safety Early Warning Strategy of Electric Vehicle Charge and Discharge Based on Artificial Intelligence
Authors: Zhang Anyue , Gao Hui , Duan Haowei 
Published: April 2021

Abstract
With the large-scale development of electric vehicles, in order to reduce the potential safety hazards in the charging process of electric vehicles, in this paper, through analyzing the characteristics of the faults in the charging process of electric vehicles and the charging and discharging fault location of electric vehicles based on artificial intelligence, a fault location method based on theoretical information fusion is proposed. The fault location method mainly includes three key modules which is fault data acquisition, address label analysis and information reverse traceability. Through the analysis of the hidden dangers of the electric vehicle charging process, the corresponding early warning process is analyzed by the established integrated safety protection model for electric vehicle charging. In this way, the structured design of the early warning system and the construction of the integrated charging and discharging safety early warning process for electric vehicles can effectively improve the charging safety of electric vehicles and promote the healthy development of electric vehicles.
Link Redundancy Research and Reliability Analysis Based on Dual CAN Architecture
Authors: Feng Yangsong , Li Hong 
Published: April 2021

Abstract
Aiming at the problem that special vehicles still need to ensure the reliability of vehicle-mounted bus communication in the long-term complex and strong interference communication environment, a link layer redundancy scheme based on dual CAN channels is proposed. By adding redundant modules and improving the redundancy mechanism, dual CAN controllers can not only realize the undisturbed switching of data communication in hot redundancy mode, but also realize the function of error correction and retransmission in cold redundancy mode. Finally, a mathematical model of the reliability of dual CAN redundancy is established through the Markov state transfer chain, and this model is compared with other models for analysis and evaluation to verify that its reliability is significantly improved.
An Research on the design and optimization of shipping routes in the Arctic
Authors: Li Xiaolong 
Published: April 2021

Abstract
In the design and research of the Arctic route, the route design can be interfered by other factors. This paper studies the route design and optimization after considering the interference of external factors. According to the external environment factors to design the arctic route weather route, the first arctic route design model was established, using the method of dynamic programming. Therefore, it is necessary to continuously determine the shortest sailing time of the ship, namely the actual sailing speed of the ship. According to the sailing conditions of the ship in different environments, so that the highest degree of navigation safety can be achieved, and the route optimization problem is transformed into a multi-stage decision-making problem. The global optimal solution is obtained by looking for the local optimal solution of each stage. Combined with the original Arctic route model, the meteorological route is optimized. By separating the present period from the future period, the present and future benefits are considered together. By determining the stage index function and the optimal index function, the basic equation of dynamic programming is established. The Pareto front curve corresponding to the requirements of the shortest voyage time and the voyage safety is given, that is, the optimal solution function curve of the two objectives. The influence between the shortest voyage time and the voyage safety is weighed comprehensively, and the optimal solution is obtained by combining the actual situation of the ship during the voyage, that is, the route optimization is completed. Finally, the optimized Arctic route is simulated and evaluated in an electronic computer simulation system with similar environment.
Research on K Nearest Neighbor Skyline Query in Time Dependent Road Network
Authors: Lixiang Song , Ke Fei 
Published: April 2021

Abstract
In recent years, with the increase for query preference, skyline query in road networks has gradually become a research hot topic. In order to help users get the desired query result, we propose a new algorithm. Firstly, we select landmark heuristically and establish corresponding shortest path tree (SPT). Then we construct a skyline matrix index called time dependent matrix index (TDMI), which help us associate the updated result set with dynamic k nearest neighbor (KNN) and simplify the non-space dominance relationship better. Finally, with SPT, TDMI, KNN and dynamic pruning conditions, an algorithm, the TDMI-KS algorithm, is proposed to rapidly answer the query result. Extensive experiments using Oldenburg road network datasets demonstrate the effectiveness and the efficiency of our proposed TDMI-KS algorithms.
PA-GAN: Graph Attention Network for Preference-Aware Social Recommendation
Authors: Hou Liyang , Kong Wenping , Gao Yali , Chen Yang , Li Xiaoyong 
Published: April 2021

AbstractSocial recommendation has been recently proposed by incorporating trust relationship to alleviate data-sparsity and cold-start problems. However, most of existing works only focus on friends different contribution to model user representation. They ignore users have different preference on items, and share different preference with friends. To address these problems, in this paper, we propose a novel Preference-Aware Graph Attention Network (PA-GAN) for trust recommendation. And we design three modules: item aggregation for user, friend-preference aggregation for user and user aggregation for item to model users’ local and global preference. Experiments on two publicly available datasets shows the proposed model PA-GAN outperforms the state-of-the-art recommendation models, and improves performance greatly.
Research on Operation Mechanism optimization framework of Power Grid Business Organization under the new circumstances
Authors: Meng Weixuan , Cheng Jiaxu , Liu Jin 
Published: April 2021

Abstract
The construction of Energy Internet enterprises drives the deep coupling between external power supply service and internal specialty synergiztion of Power Grid Enterprises. Based on the situation of Energy Internet construction and the requirements of “strong front-end, big back-end” platformized organization mode,this paper explores and puts forward the framework of operation mechanism optimization of power grid business organization, which can provide reference for power grid enterprises to promote organizational reform.
Probability, Markov Chain, and their applications
Authors: Zhang Bojun 
Published: April 2021

Abstract
Probability is a branch of mathematics that focuses on the possibility of an event or the possibility of a position. Probability is significant to the field of politics since it could help people analyze and predict election results. Specifically, the Markov chain plays an important role in this because it focuses on transition of states. With election, reelection, retirement, and other elements involved, the Markov chain is helpful for predicting election results.
Extreme Learning Machine Based on Calculating the Output Weight of Partial Robust M-regression
Authors: Yao Guanghua , Wang Gaitang 
Published: April 2021

Abstract
In order to improve the nonlinear mapping capability and learning performance of extreme learning machine (ELM), a new learning algorithm called extreme learning machine based on calculating the output weight of partial robust M-regression is proposed. This algorithm introduces the partial robust M-regression into the extreme learning machine algorithm. Firstly, the hidden layer output matrix H is calculated by extreme learning machine. Secondly, the matrix H and vector Y are weighted by weighted strategy. Then, PRM algorithm is used to establish the regression model between the weighted matrix Hw and vector Yw, and calculate its regression coefficient, namely output weight of the ELM Algorithm. The proposed method predicts the Mackay’s robot arm regression and sediment concentration of the Yellow River Basin to verify the effectiveness of the method. The simulation results show that the proposed PRMELM algorithm is superior to the original extreme learning machine algorithm in prediction accuracy and generalization performance.
A Dynamic Balance Quantization Method for YOLOv3
Authors: Hua Yang , Yu Lixin , Meng Xiao , Qin Zhiyong , Chen Boxu 
Published: April 2021

Abstract
This paper describes a quantization method for pre-trained deep CNN models, and has achieved very good results in YOLOv3. The weights are quantized to int8 and the biases are quantized to int16, by contrasting the float inference result, the mAP loss is less than 0.5 %. While running neural nets on hardware, 8-bit fixed-point quantization is the key to efficient inference. However, it is a very difficult task for running a very deep network on 8-bit hardware, often resulting in a significant decrease in accuracy or spending a lot of time on retraining the network. Our method uses dynamic fixed-point quantization and adds a small amount of bit-shifts to balance the accuracy of each layer in a YOLO network. At the same time, it further shows that this method can also be extended to other computer vision architectures and tasks, such as semantic segmentation and image classification.
Design of optical sighting telescope training system based on simulated target
Authors: Li Menglei , Rong Yingjiao , Wang Shirui , Wang Yuchen , Sun Ping 
Published: April 2021

Abstract
In order to solve the practical operation training problem of an optical sighting telescope in certain weapon system, the application research of simulated target technology is carried out. According to the tactical and technical characteristics of sight and battlefield target, based on the embedded simulation framework, a series of simulation target technologies, such as constructing the optical characteristic model of target, fitting the target trajectory, encapsulating the characteristic information of target in the typical scene, are used to explore the technical route of actual combat training relying on actual equipment and simulation target, and realize the training scene and its model. The application results show that the method is feasible.
Research on servo motor motion control system based on Beckhoff PLC
Authors: Zhang Shuai 
Published: April 2021

Abstract
In order to meet the requirements of high precision, reliability and real-time control of biaxial motion.Taking Beckhoff PLC CX9020 and Yaskawa Σ - 7S series AC servo controller as the control core, using Yaskawa SGM7J series motor and servo driver matching, the X-Y double axis servo motion control system is designed, and the overall hardware design of the control system is carried out. This paper focuses on the parameter setting, analog and digital acquisition, programming and HMI interface setting of servo motor control system. The debugging operation shows that the system runs stably and meets the control requirements of accurate positioning.
Development Trend of TV Drama Aesthetic Form from the Perspective of Big Data
Authors: Pan Yitong 
Published: April 2021

Abstract
After entering the era of big data, the aesthetic form of TV plays is bound to enter a new round of changes. It is of great strategic significance to conduct in-depth theoretical research on the changes of its development in time. Based on this, this paper analyzes the development trend of TV drama aesthetic form from the perspective of big data. In the research, we use the method of literature analysis, through consulting relevant information to understand the aesthetic form of Chinese TV drama, and summarize the current situation of aesthetic form of Chinese TV drama. At present, the development of online drama has a great impact on the aesthetic form of TV drama. Therefore, we use the form of questionnaire survey to compare and analyze the aesthetic form of traditional TV drama and online drama, and use cluster analysis to sort out and evaluate the research results, so as to obtain more direct research results. The results of this study show that the traditional TV series score 61 on openness, 65 on topic, 54 on convenience and 71 on diversity. On the other hand, the score of online drama is 90 in openness, 86 in topic, 83 in convenience and 86 in diversity. Thus, it can be seen that the network drama has a great influence on the development of people’s TV drama aesthetic form.
Innovation of Business Management Model Based on Big Data
Authors: Zhang Linqiang 
Published: April 2021

Abstract
With the development of network technology and the advent of data and information era, China’s business administration in the face of modern increasingly complex diversification of market subjects and market order is increasingly complex, it needs to require the industry and commerce departments in the performance of functions, service market subjects and other aspects to make unremitting efforts. Today, with the rapid development of Internet economy, the business administration mode is studied based on big data technology. The working mode and mode of the business administration department are analyzed through data statistics and investigation, and the current situation and problems of the service of the business administration department are analyzed and summarized and prospected.
New Coronary Pneumonia Based on Big Data and China’s Control of the Epidemic
Authors: Luo Lixia 
Published: April 2021

Abstract
With the explosive growth of data, the value mining of massive data cannot be realized by traditional technology. This article mainly studies the spread of new coronary pneumonia based on big data and China’s control of the epidemic. In this study, the location information and time information in the records of each strain were saved in two txt files respectively to prepare data for the establishment of influenza virus transmission network. According to the number of groups participating in the evolutionary game, we will get the corresponding number of replication dynamic equations nonlinear. The stability of the ordinary differential equation system can be used to judge the stability of the nonlinear evolutionary dynamic system of each group in the game model. The χ2 test was used to compare the related rate and composition between groups, and the Pearson correlation was used to analyze the correlation between inoculation rate and morbidity. Experimental data shows that before the epidemic, the average time used for the Schulte grid of college students was 28.10 seconds, with a standard deviation of 5.492. The results show that with the continuous deepening of theoretical research and practical applications, national cyberspace governance will definitely accelerate its development and become more complete.
Research on Music Assisted Teaching System Based on Artificial Intelligence Technology
Authors: Zhang Xiaochen , Yang Dayu 
Published: April 2021

Abstract
With the advent of the information age, computer technology has been greatly developed, especially the development of Artificial Intelligence(AI). And with the passage of time, AI began to involve various fields, music education is no exception. In this paper, after a detailed understanding of some research results of AI on music assisted instruction system, we mainly analyze the students’ video, audio and other related information, and save it in the database. This paper first introduces the evaluation process by using AI technology. In fact, it is necessary to find out the relationship between the influencing factors and evaluation of music assisted teaching system. Neural network(NN) is actually a model proposed by simulating the way people think in the brain. It has no strict requirements for data distribution. In terms of nonlinear data processing method, robustness and dynamics, it is very suitable to be used as a model for evaluating music assisted instruction system. Then each factor is taken as the input parameter of the NN. According to the evaluation index of music teaching, a special modeling system is designed. With the help of technical personnel, we obtained the sample data of music performance and completed the neural training. The experimental results show that the development of AI technology has broken the original situation of traditional teaching, especially the application of music system and intelligent music software based on AI in music teaching.
Design and Implementation of the Data Detection System of Business Administration Discipline Based on Data Mining
Authors: Li Miaomiao 
Published: April 2021

Abstract
Our times are developing rapidly. Because of the rapid development of computer technology, so led to the development of the times. So, in school all kinds of big and small things are basically replaced with computer systems to deal with. But it also has the effect that the data is redundant, so we need to use data mining algorithms to get the data we need. Therefore, the purpose of this paper is to use data mining algorithm, to build a data detection system for business administration disciplines, and to evaluate and simulate its experiments. So, we effectively extract the reasonable data we need based on the security of all kinds of data, to package it and aggregate analysis. Finally, after investigation to measure the accuracy of this system. Experimental results show that the system can greatly reduce the workload of the teaching system, and accurately complete the work, compared with the previous speed of about 120% faster and more accurate, by the teachers and students alike.