bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of Intelligent School P.E.in the Era of AI
Authors: Mei First author: Yang , Liuqing Second Author: Ouyang 
Published: December 2020

Abstract
In this paper, through the analysis of the future development of school physical education (P.E.) in the era of AI, new theories and research ideas are provided. The idea of intelligent education advocates the idea of lifelong education and happy learning, and the corresponding idea of P.E. should also be the idea of lifelong sports, happy sports and intelligent sports. In the era of AI, the application of modern P.E. is different from the traditional P.E. The teachers’ explanation and demonstration can effectively improve the students’ interest in learning and improve the teaching effect. The teachers become more effective in guiding the students’ learning, improving the ability of the P.E. teachers to update their knowledge and use P.E. technology, improve the quality of P.E. and P.E. evaluation system.
Federated Diabetes Mellitus Analysis via Homomorphic Encryption
Authors: Xie Yun , Li Peng , Zhu Xiang , Wu Qiuling 
Published: December 2020

Abstract
Diabetes is one of the most extensive chronic diseases in the world. Most patients suffer from the disease and its complications for a long time due to the lack of accurate and standardized treatment at early stage. Therefore, by analyzing diabetes data and establishing relevant predictive models, it is very meaningful to give reasonable health advice to high-risk groups. The establishment of an accurate prediction model requires a large number of data sources as support, and multiple medical institutions participate in data contribution and collaborative learning. Federated learning provides a secure general architecture for distributed collaborative learning. However, in the process of federated learning, the data of participants is still subject to the risk of security attacks or indirect information leakage. For example, when a participant uploads the local model parameters to an honest but curious cloud server, the cloud server can obtain relevant information from the participant. In order to solve this problem, this paper proposes a federated forest algorithm based on homomorphic encryption to strengthen the protection of the data privacy of the participants while ensuring that the accuracy of data analysis does not decrease. Analysis proves that our algorithm has good performance in privacy protection and prediction accuracy.
The Effectiveness of Autonomous Listening Study and Pedagogical Implications In the Module of Artificial Intelligence
Authors: Hu Jiahao , Hu Xujie 
Published: December 2020

Abstract
The artificial intelligence (abbreviated as AI) technology promotes the change in listening studying. An AI robot named as King of Listening Study is applied. To assess its effectiveness, an interview was made with the learners. The AI-aided study was more effective in promoting learners’ confidence. The inclusion of AI functions arouses students’ motivation to improve listening skill. Some points needs discussing. Listening can be integrated with speaking. Visual support is necessary. The investigation also indicates some pedagogical implications.
Application of Machine Learning and Real-time Feedback System to Predict Arriving Time
Authors: Guo Kaiwen 
Published: December 2020

Abstract
Real-time location feedback provides customers with direct information about the location of the package, which also provides one of the factors needed to predict the arrival time. This is not a simple math problem once we have the real-time location data because using math equations could not account for different situations that might occur to packages, which would again provides a inaccurate delivery time. By using machine learning, based on the data collected earlier and the information about the location from the use of real-time location feedback system, computers would be able to predict a delivery time with different factors involved, including weather and the possibility of the accident happened on the way. Using those different skills and factors, customers would be able to get an arrival time that is more precise than before and able to do decisions based on that. The main idea of this article is to predict the delivery time with the combination of the real-time location feedback system and machine learning.
Construction of entanglement-assisted quantum MDS codes
Authors: Guo Guanmin , Li Ruihu , Zheng Youliang , Zhang Mao 
Published: December 2020

Abstract
Entanglement-assisted quantum error correction codes (EAQEECs) can improve the reliability of transmission, which make use of pre-existing entanglement between two parties. In this paper, we construct two classes of entanglement-assisted quantum maximum distance separable (EAQMDS) codes via generalized Reed-Solomon (GRS) codes. Consequently, the results show that some of these EAQMDS codes have much larger minimum distance than ones available in the literature. Meanwhile, many of these EAQMDS codes are new in the sense that the parameters of these codes are not covered by the previously known ones. (Abstract)
Multi-View Deep Clustering based on AutoEncoder
Authors: Dong Shihao , Xu Huiying , Zhu Xinzhong , Guo XiFeng , Liu Xinwang , Wang Xia 
Published: December 2020

Abstract
In recent years, with the development of deep learning, replacing traditional clustering methods with subspaces extracted by deep neural networks will help better clustering performance. However, due to the instability of unsupervised learning, the features extracted each time are different even if the same data is processed. In order to improve the stability and performance of clustering, we propose a novel unsupervised deep embedding clustering multi-view method, which treats multiple different subspaces as different views through some data expansion methods for the same data. Specifically, our method uses a variety of different deep autoencoders to learn the latent representation of the original data and constrain them to learn different features. Our experimental evaluations on several natural image datasets show that this method has a significant improvement compared to existing methods.
Demand analysis and framework function design of cyber range construction in Colleges and Universities
Authors: Yuehong Zhang 
Published: December 2020

Abstract
This paper designs the network topology design, node simulation and environment construction, and drives the cyber range in three dimensions according to the teaching and scientific research needs of colleges and Universities: the module based on the classroom, the safety challenge of self-defining schedule, and the dynamic practice based on the team, so as to meet the needs of teaching and scientific research in the construction of cyber range in Colleges and universities.
A method of accelerating CNN computation on DSP
Authors: Li Bin , Luo Ying-guang , Guo-bin Fu Luo , Peng Ye , Hu Jia 
Published: December 2020

Abstract
Because convolution network contains a lot of multiplication and addition operations, there is still room for optimization after it is implemented on the hardware platform. This paper mainly studies the method of accelerating the deep learning algorithm on DSP, proposes to improve the convolution layer calculation by using Winograd algorithm, and implements it on GPDSP. The final results show that it can increase the parallelism of data calculation and significantly reduce the computing time.
Profit Optimization for Multiuser Video Transmission over Mobile Networks
Authors: Xu Jingce , Mai Yazong , Li Jie 
Published: December 2020

Abstract
With the increasing demand for high quality mobile video, how to receive higher quality video through mobile network with lower payment is a becoming an emerging problem. Existing approaches for Dynamic Adaptive Streaming over HTTP (DASH) face tremendous challenges when applying to video streaming in devices with different resolutions. In this paper, we propose a comprehensive profit optimization model with consideration of the cost of Content Provider, traffic payment, QoE factors and the relationship between device resolution and video quality. For ensuring the optimal profit of Content Provider and End Users at same time, we formulate both of their profit as a hybrid-objective optimization model. By solving the model, the optimum bitrates and the resulting profit can be obtained through our proposed algorithm. The simulation results demonstrate that the proposed method increases the average profit of End Users in 10%-15% compared with existing approaches.
Fault diagnosis Based on An Improved Statistics Principal Analysis Similarity Factor
Authors: Yin Hongxu , Wu Yuguang , Mu Zhijun , Geng Hongbin , Li Xiaobo 
Published: December 2020

Abstract
To settle the issue of conventional principal component analysis similarity factor cannot take advantage of higher-order statistics information of process variables, we presented an improved statistics principal analysis similarity factor (SPASF) to identify fault patterns in our work. In the improved SPASF approach, process data is first converted from original space into a new statistics space by the means of statistics pattern analysis (SPA) technology, and principal component analysis (PCA) is then employed to extract principal components in statistics space. At last, the pattern of snapshot dataset is recognized by measuring the similarity of principal components derived from statistics snapshot dataset and statistics historical fault dataset. The effectiveness of suggested SPASF based approach is verified through a case study on continuous stirring tank reactor (CSTR) by the means of recognizing fault patterns of snapshot datasets.
Recommendation algorithm based on user attributes and tag preferences
Authors: Zhang Min , Yan Xiaoling , Peng Hongwei 
Published: December 2020

Abstract
Aiming at the problem of sparse data and low recommendation accuracy of recommendation systems in a big data environment, a UT-CF algorithm that combines user attributes and tag preferences is proposed. The algorithm extracts and selects user attribute information, calculates the user attribute similarity matrix according to the user attribute characteristics; obtains the user’s tag score according to each score of the user item score matrix, obtains the user tag preference matrix, and calculates the difference between user tags Similarity, to get the user’s similar neighbors; In order to reduce the search space of similar users, the K-means clustering algorithm based on the principle of maximum distance is introduced. Make the top k recommendations based on the recommendations of the target user’s nearest neighbors. Through the experiment of MovieLens 100K data set, the algorithm improves the accuracy of recommendation.
Constructions of ternary LRCs with optimal distance
Authors: Zheng Youliang , Li Ruihu , Lv Jingjie , Zhang Mao 
Published: December 2020

Abstract
As a new class of erasure codes, locally repairable codes (LRCs) can provide data repair capability for distributed storage system. In this paper, we firstly propose an effective construction method to obtain optimal LRCs based on ternary optimal codes. Then, three classes of optimal LRCs are constructed by ternary cyclic codes and constant cyclic codes. All codes we gained have the optimal distance and reach the Cadambe-Mazumdar (C-M) bound.
CenterNet-Triplets application: surveillance camera illegal management detection
Authors: Zhu Xiangxiang , Yang Li , Zhang Dongping , Chen Ping 
Published: December 2020

Abstract
There are two types of illegal business in city management: out-of-store management and illegal mobile management, we simply refer to as outmanagement and flowmagement. The main way to deal with illegal buessiness is manual management. However, this requires a lot of costs, and it is impossible for city management personnel to conduct 24hour inspections. In order to improve management efficiency and save labor costs, this article uses city surveillance cameras, combined with deep learning object detection methods, to conduct real-time detection of illegal operations. Aiming at the situation where the amount of surveillance video data is small, a data enhancement method is proposed to improve the detection effect. CenterNet-Triplets is a object detection algorithm without anchor boxes. Its backbone network is Hourglass Network. The AP of CenterNet-Triplets on MS-COCO dataset is 47.0%, and it has a good detection speed. Experimental results show that CenterNet-Triplets is suitable as a method for detecting illegal operations in city management.
Research on Cloud Architecture of Enterprise Distributed Business Information System Based on SOA
Authors: Hu Hai-rong , Fang Lu-lu , Yang Chao-hong , Zhang Yang 
Published: December 2020

Abstract
The current SOA-based Enterprise Distributed Business Information System has poor scalability and low utilization, in order to solve this problem, this article analyzes the possibility of integration between Cloud Computing and SOA after telling the similarities between the two. We proposed a SOA-based Cloud architecture model, and applied it to Enterprise Distributed Information Systems, and proposed SOA-based Enterprise Distributed Business Information System Cloud Architecture Conceptual Model and Operating Model. This scheme can well solve the problems of the current system, and can greatly improve the quality and efficiency of system development, and has guiding significance for the development direction of enterprise information systems.
A multi-objective routing optimization model for ship intelligent navigation
Authors: Gao Zhupeng , Li Yuankui , Cui Jinlong 
Published: December 2020

Abstract
In order to solve the problem of ship route planning in the autonomous navigation and decision-making of ships, based on the electronic chart display and information system (ECDIS), the meteorological conditions of ocean-going ships were analyzed and the grid method was used to establish an environmental model. The voyage time, fuel consumption and navigation safety were taken as optimization goals, and a multi-objective ship route optimization model was established, then the non-dominated sorting genetic algorithm with elite strategy (NSGA-II) was applied in the optimization route searching to realize the solution of the optimal ship route.
Research on Digital Mapping Method Based on Complex Terrain
Authors: Zhu Pengpeng , Cui Hengle , Zhang Zipan , Li Jian , Ding Xiaopeng 
Published: December 2020

Abstract
Based on UAV low-altitude remote sensing technology and tilt photography technology, combined with highway projects, the UAV operating process, quality inspection and accuracy, and positioning accuracy in the mountainous complex terrain conditions are analyzed, and operating experience is summarized for subsequent navigation. The production provides guidance. At the same time, it also provides a reference for the exploration of digital mapping methods, and lays a theoretical foundation for the research of mapping methods. The combination of mapping methods in complex terrain with high technology is highly innovative.
Application of machine learning on the modelling of diffusion Magnetic Resonance Imaging signal
Authors: Ye Chen , Xu Daoyun , Wang Lihui , Zhu Yuemin 
Published: December 2020

Abstract
The modelling of diffusion Magnetic Resonance Imaging (dMRI) signals is very important for medical clinical application. However, the traditional method is to use a fixed mathematical model to make assumptions about the diffusion-weighted (DW) signals of all regions of human organ, which is unreasonable. In this paper, Convolutional Neural Network (CNN), a machine learning based method is used for learning the different characteristics of the signals, and finally intelligently give multi-model predictions for different regions of human livers. The performance of the proposed method is verified on both simulation and real liver data. The results show that the multi-model predicted by CNN method has high performance in distinguishing normal liver from diseased liver, and has great clinical application prospects
Protein function module of deep learning and PPI network prediction
Authors: TaoLu  , Huo Sicong 
Published: December 2020

Abstract
The outbreak of new coronavirus in 2020, the development of bioinformatics technology has become a new hot spot, in which the prediction of protein function module has become a research hotspot. The existing algorithms for predicting protein functional modules have the disadvantages of low accuracy and few functional modules. In this paper, an algorithm for deep learning and PPI network prediction of protein functional modules is proposed. Firstly, three important attributes of PPI network (node location, PPI network structure and core node) are integrated. Then the improved density algorithm is used for clustering analysis, and then the improved isometric mapping dimension reduction algorithm is used for principal component analysis. Finally, MLP is used for training The protein function module was obtained. The accuracy, F value, dimensionality reduction rate and the number of protein functional modules were compared among the other three PPI network based algorithms acc-fmd, MCL and mcode. The experimental results show that IIMP algorithm is better than other algorithms in accuracy, F value, dimension reduction rate and number of recognition modules.
Transient analysis of the fault process of double Y-type three-phase asynchronous motor during open phase operation
Authors: Hailing LU , Chaohui ZHAO , Hebiao SHEN , Licong DUAN 
Published: December 2020

Abstract
In order to reduce the loss caused by the failure of large motors, it is of great significance to accurately detect and predict the state of the motor during operation. Based on the theory of motor asymmetry, combined with the influence of temperature on the winding resistance in the actual operation of the motor, with the help of ANSYS finite element analysis software, a simulation model of a large three-phase asynchronous motor with double Y windings is established. This article uses the Maxwell circuit editor module to build an external circuit. First, verify the abnormal characteristics of the three-phase current when the phase is missing through simulation, and then analyze the influence of the resistance asymmetry on the motor operating characteristics. The simulation results show that the abnormality of the three-phase current caused by the winding asymmetry is regularly and cyclically amplified, that is, in the actual motor operation, the first phase of the abnormal winding can be determined by real-time observation of the three-phase current. In this way, we can avoid the burning of the motor.
IoT based cloud monitoring system for high-voltage power distribution room in the electric substation
Authors: Wu Shuifeng , Mao Wenqi , Li Gang , Gan Shengliang , Liu Yun , Yan Ying 
Published: December 2020

Abstract
Under the background of developing smart grids and ubiquitous electric Internet of Things (UEIoT) in China, an IoT based cloud monitoring system for high-voltage power distribution room in the electric substation has been proposed in this paper as a typical pioneer project. This system includs perception layer, network layer, application layer. The Key technologies, which contribute to realize real-time automatic control, intelligent regulation, online analysis and decision-making, are illustrated in detail. The proposed IoT based cloud monitoring system has been conducted in several areas of Hunan province and the field application illustrates the benefits of this system in the aspects of reducing manual operation, raising efficiency and preventing accidents.
Research on Simulation and Planning of Combustion Chamber in Gas Turbine
Authors: Liu Zeyuan , Lv Rongxin , Zuo Jiankai , Chen Jiatong , Yin Xiangquan , Zhao Xinyu 
Published: December 2020

Abstract
A gas turbine is an internal combustion power machine that drives the impeller to rotate at a high speed with a continuous flow of gas as the working medium, and converts the energy of the fuel into useful work. Nowadays, all developed countries attach great importance to the development of gas turbines, and the combustion chamber of the gas turbine is an important part of it. If the combustion chamber is properly optimized, the combustion efficiency will be greatly improved. The change of the number of blades of the gas turbine will have a great influence on the combustion effect inside the combustion chamber. In this paper, the gas turbine blade values are modified to study its influence on the combustion chamber flow field characteristics and methane mixing characteristics. Finally, when other conditions are constant, the larger the value of the blade, the more conducive to the formation of the return zone of the combustion chamber and the mixing of gas, and the better the combustion effect in the combustion chamber. This study also provides a reference for the design of gas turbine combustor.
X-band RCS Reduction of Airborne Antenna Array Using Loaded PIN Diodes
Authors: Wu Zijian , Wang Fei , Zhou Jianjiang 
Published: December 2020

Abstract
The paper applies PIN diodes to airborne microstrip antenna array to achieve radar cross section (RCS) reduction while the array is not at work, thus enhance the stealth performance of the aircraft. PIN diode can be regarded as a variable resistance or capacitance when it is forward-biased or reverse-biased, respectively. The equivalent resistance value of PIN diode in the forward-biased state is optimized and the radiation performance of the antenna array is not seriously affected. Simulation results show that the method can dramatically reduce the antenna RCS by disturbing the electric field distribution of current incident wave. The experiment results depict that maximum RCS reduction of aircraft model is more than 25dB, thus validates the proposed method.
Research on the Influence of Electromagnetic Radiation in the Automobile on the SAR Value of Human Body
Authors: Wang Jian , Mu Sikai , Hai Zhenyang 
Published: December 2020

Abstract
At present, the research on Evaluating Systematic Electromagnetic performance of automobile has gradually become a heated topic. With the deepening of research, personal safety in electromagnetic environment has been paid increasingly more attention. In terms of the complexity of the electromagnetic environment in the automobile, the automobile pole antenna, the wiring harness, the DC-DC converter, and the electromagnetic field distribution is simulated by the three-dimensional electromagnetic simulation software EMC studio, then the electric field intensity and the XZ plane current distribution at different seat positions can be obtained, and the SAR value absorption of human body at different seat positions and different human body sizes in the automobile is also calculated. The simulation results show that the electromagnetic radiation in the automobile will not do harm to the human body, which provides reference for the later research of human electromagnetic safety in electric automobile.
Instance segmentation model CP-Condinst
Authors: Yuan Jianye , ding Xinwang , Sun Lele 
Published: December 2020

Abstract
With the rapid development of deep learning image recognition technology, people have higher and higher requirements on the accuracy and speed of segmentation recognition. So instance segmentation becomes more and more important. The accuracy and speed of segmentation recognition are increasingly demanded. Recently, Shen Chunhua et al. proposed a very quantized instance segmentation model CondInst. In the paper, which used CSPResNet instead of ResNet, and added a Prediction module method to the head to improve model accuracy. Finally, on the MS COCO2017 data set, the simulation results show that CondInst’s AP, AP50, AP75, APS, APM and APL were increased by 0.2%, 0.4%, 0.3%, 0.1% and 0.4% respectively in the 50 layer residual model of our model, compared with CondInst’s AP, AP50, AP75, APS, APM and APL.The experimental results show that the method has certain practical value.