bluuebunny/crossref_metadata_2025_split huggingface dataset:
Forecasting model of electricity market prosperity index based on multidimensional big data
Authors: Liu Jia , Ma Rui , Yan Zhenhua , Jia Lu , Wang Liang , Shi Yuanyuan 
Published: April 2021

Abstract
The establishment of electricity market prosperity index prediction model is of great significance to the planning, construction, production and decision-making of electricity market. On this basis, from the perspective of industry and industry, this paper puts forward the design of multi-dimensional big data forecasting model of power industry electricity market prosperity index. This paper analyzes the compilation process of the prosperity index of the power industry, studies the pre-processing method of the power data based on the multidimensional big data. Based on the index division algorithm of multi-dimensional big data time difference analysis and the method of compiling the prosperity index of the power industry, the prosperity index of the power market of the power industry is predicted. Through the calculation of the diffusion index method and composite index of power consumption in power industry. Combined with the multi-dimensional big data nonlinear analysis method, it is applied to the prosperity index analysis of the power industry. Finally, the accurate prediction of the electricity market prosperity index of the power industry is realized. Finally, through the simulation test, according to the power data provided by the electric power bureau of a city, the power prosperity index is compiled, and the forecasting effect of the power industry electricity market prosperity index is tested. Experimental results show that the model has high effectiveness in practical application, and can fully meet the research requirements.
Simulation of auto obstacle avoidance based on Unity machine learning
Authors: Jiangyuan Qi 
Published: April 2021

Abstract
This article mainly introduces the use of the TensorFlow platform, through the Unity 3d engine, a 3d rendering tool, to simulate a car in a 3d world using TensorFlow training and learning, which can avoid obstacles in front and stay in the lane by itself, thus realizing a private Unity 3d for machine learning Applications.
Research on the Innovation and Application of Cloud Technology in the Construction of Management Laboratory
Authors: Qi Yuanfang 
Published: April 2021

Abstract
With the rapid development of information technology, cloud technology is widely used in various industries. In the field of education and teaching, colleges and universities have invested more in the application of cloud technology in laboratory construction. The application of this technology has greatly improved the management effect of university laboratory, improved the overall quality of teaching and promoted the development of school education. This paper focuses on the typical application of cloud technology in the construction of economic management laboratory, and puts forward some existing problems and prospects for the future of cloud technology in the construction of economic management laboratory, so as to promote the cloud technology in the field of education to achieve better development.
Improved Smoking Target Detection Algorithm Based On YOLOv3
Authors: Wei Zhao , Zhu Yan Xu , Li Qi Xuan , Wang Cai 
Published: April 2021

Abstract
For the detection of smoking behavior, in order to make accurate state judgments, a smoking target detection algorithm based on the improvement of YOLOv3 is proposed. By introducing Mosaic data to enhance the background of the rich image, the batch_size is increased, and Mish is used as the activation function. To improve the generalization ability of the model, the DIOU_Loss function is used as the activation function to effectively use the information of the center point of the bounding box to improve the accuracy of target detection. Through self-made smoke detection data sets, experiments are carried out, and the experimental results show that the algorithm can accurately detect To smoking behavior, improve the MAP and AP of training.
Single Image Super Resolution Enhancement Method Based on Fractal Interpolation of Iteration Function System
Authors: He Sihua , Wu Chunguang , Liu Chenghao , Wang Zhefu 
Published: April 2021

Abstract
Based on the similarity and transitivity of local features of image data, self similarity and scale invariance of fractal features, fractal method of Iterated Function System is used to construct fractal interpolation function, and variable vertical scale factor is used to realize super-resolution enhancement of two-dimensional image data. The experimental results show that fractal interpolation method of iterated function system can keep the image details better, and the reconstruction error is smaller, which is better than bilinear interpolation and bicubic interpolation methods.
Research on Privacy Protection Based on Block Chain in Internet of Things Network
Authors: Chun Wang , Li Chen 
Published: April 2021

Abstract
The block chain technology has brought a dramatic change in the Internet of Things network, especially in personal privacy protection, due to the structural characteristics of decentralized, smart contract and immutability. This paper starts with the analysis of characteristics of block chain technology, further analyses the privacy protection problems in IOT network, then propose a personal data management system model to solve the problem.
The influence of LEO satellite Doppler effect on LoRa modulation and its solutio
Authors: Cao Cong , Zhai Shenghua 
Published: April 2021

Abstract
In order to explore the adaptability of LoRa technology in low Earth orbit satellite communications, this paper studies the influence of the Doppler effect on LoRa modulation in satellite-to-Earth radio channel, and finds that the larger the LoRa modulation spreading factor, the more vulnerable to the Doppler-Rate. The LoRa technology did not estimate and compensate the Doppler-Rate when it was designed. Therefore, this paper proposes a Doppler-Rate estimation algorithm that can better adapt the LoRa technology to low Earth orbit satellite communications.
A Quantum Multi-Agent Blind Signature Scheme with Unordered Signatures
Authors: Chen Jingjing , You Fucheng 
Published: April 2021

Abstract
With the development of information society, network security problems are becoming more and more serious, and digital signature as an authentication method is widely used in many fields such as information security and e-commerce, mainly to solve security problems such as forgery, repudiation, impersonation and tampering [1]. The latest quantum technology can overcome a series of difficult problems of classical digital signatures in a very short time. In this paper, we propose a quantum multiproxy blind signature scheme, in which the original signer is unable to sign the document himself, and delegates multiple proxy signers to sign and verify the document, and the signature order of the proxy signers is not restricted. The scheme adopts techniques such as Unitary transformation and Hadamard gate, and uses quantum key distribution protocol to ensure its security, which improves the efficiency of signature, ensures the security of signature, and has certain anti-attack capability.
Optimization design method of human computer interaction mode for basic Japanese teaching software
Authors: Sun Hui 
Published: April 2021

Abstract
In order to solve the problems of less teacher-student interaction and large amount of exercises in modern teaching process, the optimization design method of human-computer interaction mode of basic Japanese teaching software is optimized. Firstly, the overall structure and function of the basic Japanese teaching software are analyzed, including the parameter selection in the chain drive design; secondly, the basic Japanese teaching software is designed in detail, and the innovation of the software in the design process is analyzed; finally, the functions of the designed software are realized. The running example of the system shows that the designed man-machine interactive chain drive assistant teaching software can improve the man-machine interactive experience and is accepted by teachers and students.
Hierarchical Prediction of Infrared interference Environment based on Mutual Information and Random Forest
Authors: Wu Xin , Wu Youli , Chen Bian , Gan Yuepeng , Cai Yuxuan 
Published: April 2021

Abstract
In order to realize the classification prediction of environment in the process of combat, we set up the classification prediction model. First, we divided data set into blocks because of the big scale, wide range of influencing factors and strong nonlinear relationship among them. The missile hit rate is reflected in the miss distance, clustering each block data to analysis the correlation factors that affect the miss distance. Second, according to the mutual information theory, the strong correlation factors affecting the miss distance are identified from three aspects which are throwing strategies, maneuver mode and the relative situation by calculating the mutual information matrix. Third, the strong correlation factors as the sample attributes of node splitting in the decision tree, a hierarchical prediction model of infrared interference environment is constructed. This method can better identify the strong correlation factors that affect the miss distance, and avoid the adverse impact on the classification prediction due to the difference of correlation factors. The results show that the method is suitable for the processing of miss distance data. The prediction accuracy of infrared interference environment classification is about 98%, and the generalization ability of the model is strong.
Few-Shot Grape Leaf Diseases Classification Based on Generative Adversarial Network
Authors: Zeng Mingzhao , Gao Huiyi , Wan Li 
Published: April 2021

Abstract
The treatment and prevention of crop diseases have an extremely important impact on the yield and quality of crops. In recent years, with the development of computer vision and deep learning technology, research on crop disease recognition based on leaf images has received extensive attention. In the field of grape disease recognition, the lack of large-scale diseased leaf labeling data sets limits the accuracy of recognition, and obtaining professional grape disease data sets requires a lot of manpower and material resources. Aiming at the problem of the lack of grape leaf data set, this research proposes a data generation model based on the cycle Generative Adversarial Network model which introduced an leaf foreground module (LFM) block. Experiments show that the model can generate high-quality grape leaf disease images, which can improve the accuracy of grape disease recognition task in a Few-Shot Grape Leaf Diseases Classification task.
A study of design and digital simulation of a anterior internal fixation system
Authors: Cao Fu , Fu Rongchang , Wang Wenyuan 
Published: April 2021

Abstract
At present, anterior cervical discectomy and fusion often use internal fixation system to assist fixation of surgical segments, and the design of internal fixation system profile has an important influence on postoperative complications such as vertebral injury. In this study, a triangular anterior internal fixation system was designed to reduce the risk of postoperative complications. In this study, a intact C1-C7 segment finite element model was established and verified. Based on this model, a quadrilateral fixation system (QFS) and a triangle fixation system (TFS) implanted model were established. The stress distribution and peak value of the screw holes were compared during the postoperative flexion, lateral bending, rotation and extension motion patterns. Compared with the QFS, the stress ratio of the bone-screw interface of TFS is 40% smaller on average. After TFS implantation, in addition, because of the TFS screw hole beard less stress, it has certain advantages of screw loosening and reducing the risk of vertebra damage at the screw hole.
Research on the Application of DevOps in the Smart Campus of Colleges and Universities
Authors: Pingrong Lin , Xiaoquan Shi , Junqin Yang 
Published: April 2021

Abstract
With the advancement of the construction of smart campuses, legacy systems coexist with new systems, and frequent updates and iterations of new and old systems, which consume a lot of manpower and material resources. In order to meet the needs of a fast iterative system, a set of DevOps application schemes based on smart campuses are proposed. The plan fully considers legacy system transformation scenarios, and builds the DevOps platform through ZenTao, Jenkins, SonarQube, Elastic, Docker, Zabbix and other technologies to provide a good communication channel for development and operation and maintenance teams, reduce development iteration costs, and realize automated management. Practice has proved that the solution greatly shortens the delivery time of the system and improves the delivery quality, which can continuously promote the development of products and facilitate the construction of smart campuses.
Research on Skin Roughness Classification Based on Improved SVM
Authors: Wang Wei , Li He 
Published: April 2021

Abstract
Skin condition closely reflects human health, and skin roughness is one of the factors to analyze skin condition. This paper presents a classification algorithm of skin roughness based on improved support vector machine. The improved gray co-occurrence matrix, Tamura texture feature, fractal dimension, Gabor feature and other 24 properties affecting skin roughness were extracted by using human skin samples captured by visible light as data sources. Genetic algorithm was used to optimize the feature space, and the most important feature combinations were selected according to the fitness values. In order to improve the performance of classifier, the improved particle swarm optimization algorithm is used to optimize the penalty factor C and radial kernel function coefficient Gamma of support vector machine. The experiment shows that the model established in this paper has an obvious effect on the classification of skin roughness.
An integrated approach based on two-objective optimization and 2SFCA model for health-care facility location-allocation problems
Authors: Song Yi , Liu Lu , Xia Lei 
Published: April 2021

Abstract
Public health-care facilities are essential to all communities, and their location has long been an important issue in urban planning. With the steady growth of Shenzhen’s population, the government needs to decide where health-care facilities should be located to improve the equity of accessibility, and further more to raise the residential needs and satisfaction for the entire population. As a large immigrate city, Shenzhen is a complex socio-ecological system which make it hard to find the best solution that meets all of the objectives. In this article, we establish a multi-objective model to examine the problem of where health-care facilities should be located to improve accessibility for the entire population and decrease the cost of building new facilities. In particular, the model accounts for the different requirements of varying age groups. We used data from Shenzhen and two heuristic algorithms to create the model. The strategy proposed here can be adopted in urban planning and help policymakers reach better decisions.
Node deployment method of Intelligent smoke sensors across high space using many-objective optimization algorithm
Authors: Liu Pingshan , Fang Junli , Huang Hongjun 
Published: April 2021

Abstract
NB-IoT promotes innovation in the field of wireless sensor networks[7], the problem of deployment and coverage optimization of intelligent smoke sensors based on NB-IoT technology in high space has become a new research trend subsequently. In this article, we study the problem of the node deployment and coverage optimization of intelligent smoke sensors in a tall indoor space environment. Firstly, an intelligent smoke sensor nodes deployment method suitable for tall spaces is designed through the analysis of the tall environment and the particularity of the intelligent smoke sensors. Then we establish a three-dimensional directed coverage perception model. The node coverage range and deployment cost are the objective functions, which need to be optimized. Furthermore, a node deployment approach across specific high space is developed on the basis of multi-objective optimization algorithm to optimize the two objective functions. Compared with existing deployment schemes, the results of simulations demonstrate that our proposed deployment strategies can achieve better Quality of Coverage (QoC) and detection performance while enlarges the monitoring scope.
SSM-based Intelligent WeChat Applet for Pick-up
Authors: Huang Zijie , Zhao Hongzhou , Lin Jianhui , Liu Minling , Zheng Huanxin 
Published: April 2021

Abstract
With the booming of e-commerce, the courier industry has grown rapidly. Students, as the main force of online shopping, make colleges and universities become the gathering place of express delivery, and the campus express service emerges as the times require. Nowadays, campus courier service is not complete. There are many problems such as scattered courier sites, distance between courier sites and dormitories, conflict between students’ courier collection time and class time, and long courier collection cycle, which makes teachers and students have strong need for courier pick-up. In this paper, we design an SSM-based intelligent WeChat applet to meet the need of students and teachers, which improves college courier services, enhances the quality of campus life; and solves the last mile problem of the courier service on campus.
Regional research based on big data of hot spring industry in Dawan District of Guangdong, Hong Kong and Macao
Authors: Zhang Bo , Zhang Zhiqin 
Published: April 2021

Abstract
One belt, one road area, tourism research in the Tai Wan District of Guangdong, Hong Kong and Macao is mainly focused on the integration mechanism of culture, commerce and tourism in Guangdong, Hong Kong and Macau, and the leisure and recreational zone structure of the city group of Guangdong, Hong Kong and Macau. In this paper, combined with the actual needs of hot spring scenic spots in Guangdong, Hong Kong and Macao, we use the concept of global tourism to achieve information integration, marketing accuracy, intelligent management, and create humanized service, so as to create a new benchmark of smart hot spring scenic spots. We need a support platform and four kinds of application-oriented business systems, including technical service, technical management, technical marketing, and technical management. Using the methods of theoretical analysis, simulation test or implementation, this paper studies the regional scientific and technological research based on the scientific and technological big data of hot spring industry in Dawan District of Guangdong, Hong Kong and Macao.
Design and Validation of an auxiliary automatic driving system for tram track cleaning vehicles based on ROS
Authors: Ma Xizhe , Fang Zhongbing , Liang Kaizhou , hu Jianshuo , Wu Qiantong , Huang Xiaojie , Xie Yongjun 
Published: April 2021

Abstract
This paper aims to improve the automation of tram track cleaning vehicles by applying cutting-edge technologies from multiple disciplines such as positioning and navigation, environment perception, compliance operation, and real-time obstacle avoidance to conventional tram track cleaning vehicles. At present, there is no relatively perfect solution for the automation of tram track cleaning vehicles at home and abroad. This paper gives a reference for hardware selection and provides design ideas for software design when designing the tram track cleaning vehicle automatic driving. A set of test tracks and rules for the automatic driving system is designed on the simulation platform for the use scenarios of tram track cleaning vehicles, and the feasibility of the research system in this paper is verified on this basis.
Researching for Teaching Method of Big Data Curriculum Group
Authors: Zhou Jingjing 
Published: April 2021

Abstract
artificial intelligence, as the most popular technology in the field of science and technology, has attracted the attention of many people inside and outside the industry. Artificial intelligence has been paid more and more attention in the field of computer, and applied in robot, economic and political decision making, control system, simulation system. Based on the training of applied ability, this paper discusses the teaching contents and teaching methods of big data curriculum group.
A Course Construction Mode of “One Body-Two Wings” for Software Engineering Major under Background of New Engineering
Authors: Yu Jiujiu , Zhang Jishan , Chen Yun , Wu Ning , Mei Yingying , Zhang Deqing , Zhu Canglu , Zhu Lili 
Published: April 2021

Abstract
The research is based on the background of new engineering, taking the software engineering major of Anhui Sanlian University as an example, this study is devoted to exploring and implementing on a feasible mode of “one body-two wings” for course construction of software engineering major. Talents cultivation based on integration of industry and education is “one body”, curriculum design and case resource construction are “two wings” of the mode. Furthermore, further work is put forward to optimize for this mode from three aspects.
Analysis on the Application of Big Data Technology in Medical and Health Industry
Authors: Qinghua Chen , Weiguang Wang 
Published: April 2021

Abstract
With the advent of the information age and the continuous development of big data technology, big data technology is widely used in the medical and health industry. By using big data technology, the data of medical and health industry can be collected, integrated, analyzed and shared intelligently. The structure system of medical data can be established. It can not only improve the utilization rate of data, but also realize a better grasp of the residents, thus promoting the continuous development of medical services. Based on this, this paper will analyze the concept of big data, combined with the characteristics of medical big data, explore the application of big data technology in the medical and health industry, hoping to provide a reference for the future development of the medical industry.
Research on Interdisciplinarity-Teaching of Digital Media Art Under Big Data
Authors: Tong Yuanyuan , Wu Jie , Zhang Xinyue 
Published: April 2021

Abstract
Digital media art has been developed unprecedentedly under the influence of big data, but its development also has some problems such as technology shortage, backward technology and lack of big data support etc. Especially, the cross application in media technology has not been deeply developed and used interactively, so that media technology is hindered in the big development. As a combination of technology and art, digital media art itself is a data crossover, and the study of its cross teaching in the context of big data is conducive to the healthy development of digital media art education and the industry. In this paper, we analyze the problems related to the current teaching situation of digital media art, conduct research from the application of interdisciplinary courses within the discipline, seek solutions to related problems, and provide scientific guidance for the innovative development of digital media.