bluuebunny/crossref_metadata_2025 huggingface dataset:
Grid Architecture and Metadata Model of Computer Education Resources Based on Network Teaching Mode
Authors: Zhang Rongfeng
Published: April 2021

With the continuous progress of our modern science and information technology, network computer distance teaching has gradually become an indispensable part of our school education. In view of the major shortcomings of the current network physical education teaching system, such as the isolation of information resources and the difficulty of updating, the comprehensive research and application of network education information grid management technology and education metadata processing model are established. In the research and analysis, this paper selects 235 network teaching model involved in the sample survey, and through the survey of network teaching mode and traditional teaching mode multimedia, non-linear, independent comparative analysis, from the comparative results show that the network teaching mode is superior to the traditional teaching mode, not only can promote the development of computer teaching mode, but also can improve computer teaching effect. Through the investigation, it is found that both teachers and students have more than 85% recognition of the network teaching mode, which provides a guarantee for the network teaching mode.
Implementation of Computer Telecommunication Network Management and Centralized Monitoring System Based on Genetic Algorithm
Authors: Wu Haiwei , Wu Hanling
Published: April 2021

With the continuous strengthening of competition in China’s telecommunications market, the technology and management of the telecommunications industry have made great strides forward. If the system application, server or network equipment fails and there is no suitable solution, it will have a great impact on the whole business activities of the enterprise. Modern network is no longer simply providing an end-to-end service as reliable as possible, but is committed to providing users with high-quality and reliable communication service quality to meet the bandwidth delay requirements of various applications. The networking of monitoring system is one of the main goals of monitoring system development, which develops with the wide application of information technology represented by Internet. In order to improve the level of operation and maintenance, customer satisfaction and market competitiveness, enterprises undoubtedly need to speed up the construction of integrated network management system. This paper discusses the method of combining genetic algorithm and computer network technology to realize computer telecommunication network management and centralized monitoring, which is of great significance to improve the level of field maintenance and management.
Design and Implementation of Personal Medical Assistant System Based on Edge Computing
Authors: Dong Hongyan , Zhang Xiaozhong , Kong Dehua , Zhang Bo
Published: April 2021

In recent years, people pay more and more attention to health. In order to correctly respond to state of illness and reduce unnecessary medical expenses, in this paper, a personal medical assistant system based on edge computing is proposed. The overall structure and each functional module of the system are designed, and the tasks of each layer in the structure and implementation methods of each function are analyzed in detail. This personal medical assistant system can provide state analysis of illness, diagnosis and treatment advice, remote medical treatment and map navigation services, which can give a great help when people encounter diseases.
Facial Expression Recognition Algorithm Based on Convolution Neural Network and Multi-Feature Fusion
Authors: Tang Xiaoping , zhang Tianping
Published: April 2021

Facial expression recognition is a hot topic in the field of computer vision. The related research results show high application value in many fields, such as human-computer interaction, intelligent emotional robot, fatigue driving detection, medical health, safety prevention and control, teaching evaluation and so on. However, the huge difference within the expression class still has a prominent impact on the expression recognition, and it is difficult to solve this problem by single feature and traditional feature fusion methods. Therefore, this paper uses neural network and feature fusion strategy to further characterize and classify the constructed features. Experiments show that this method can effectively overcome the interference caused by the differences within the expression class, and achieve ideal expression recognition results.
Research on Centroid Localization Algorithm in Wireless Sensor Networks
Authors: Hu Pingfang , Zhang Bo
Published: April 2021

Localization of nodes is the key technology for application of wireless sensor network. The positioning accuracy of centroid localization algorithm completely depends on the size and the distribution density of anchor nodes. Anchor nodes are small and the density of anchor nodes are distributed randomly in actual application, so the positioning accuracy of centroid localization algorithm is relatively low. In order to improve the accuracy of nodes, a method which is centroid localization algorithm of wireless sensor network is proposed based on received signal strength indicator. In the algorithm, firstly, point-to-point signal intensity is computed by RSSI and converts its inverse distance as the centroid algorithm. Then the weights of centroid localization algorithm are used for calculating coordinates of the unknown node. Through the simulation algorithm for testing and analysis, the simulation results show that, compared with the traditional algorithm, the positioning error of the centroid localization algorithm is obviously reduced and the node location accuracy is improved. It is an effective localization algorithm for application of wireless sensor networks.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Intelligent control model of athletes’ training intensity based on big data analysis of physical fitness
Authors: Zhang QI-QI
Published: April 2021

Based on the analysis of big data, this paper designs the intelligent control model of sports athletes’ training progress. By constructing the evaluation system of sports athletes’ training intensity, it improves the intelligent control algorithm of sports athletes’ training intensity, and ensures the operation effect of the intelligent control model of sports athletes’ training intensity. In order to verify the effect of the model, simulation experiments are carried out, and the results show that most of the models are effective. From the perspective of analysis, the intelligent model of athletes’ training progress has outstanding practical value. The model can quickly and accurately formulate training plans, solve the data fluctuation phenomenon under the trend of big data division, and realize intelligent operation. Moreover, the fluency and accuracy are very high, which is worth promoting and applying.
Research and Implementation of Intelligent Garbage Collection Car System
Authors: Zhang Lan , Jiang Wen
Published: April 2021

With the development of artificial intelligence technology, the conditions for the introduction of intelligent garbage sorting and recycling tools in garbage sorting and recycling have gradually matured. Compared with the traditional manual recycling, intelligent garbage collection tools have considerable advantages in terms of work efficiency, personal safety and virus transmission. In this paper, an intelligent garbage collection system is designed. Through camera tracking, obstacle avoidance by laser radar and other technologies, the garbage in the area to be recycled will be automatically recycled to the designated location, and the operation and loading of the garbage car will be uploaded to the cloud server. Users can view the situation of the intelligent garbage collection system through their mobile phones or web pages.
RETRACTED: Research on E-commerce Recommendation System Based on Big Data Technology
Authors: Wan Zhenjie
Published: April 2021

With the continuous development of e-commerce and the increasing number of users, more and more scholars have devoted themselves to the research of recommendation algorithms for e-commerce platforms. How to quickly dig out information or products that users are interested in from massive data is a research category in the field of recommendation systems. The emergence of the Spark memory computing platform can provide technical support for improving the efficiency and real-time performance of the recommendation algorithm. This article first analyzes the requirements of the e-commerce recommendation system, and designs the overall architecture of the system. Through the analysis of the system, a recommendation system that conforms to the e-commerce system is designed, and a stream computing framework is used to implement a recommendation system that can meet offline and online recommendations combined recommendation system.
Research on the application of big data in regional industrial supply chain
Authors: Yajun Sun , LiLi  , Juanjuan Tian
Published: April 2021

Taking Artemisia industry, the pillar industry of Qichun County as an example, this paper studies the role of big data in the construction of regional industrial supply chain, and uses big data technology to analyze the application of modern information technology in the industry, such as the system of county industrial supply chain, product structure and e-commerce. The main problems of the industrial supply chain in Qichun County are: insufficient application of big data technology in the whole supply chain, low application of big data technology in the traditional manufacturing industry, low application of big data in the tourism and health industry, and low intelligence of the logistics system. It is proposed to use big data and other information technology to optimise the supply chain upgrade of the county industry and establish a complete industrial chain of smart manufacturing, smart logistics, smart tourism and artificial intelligence + big health.
High-Frequency Data Link System Architecture Design based on SysML Modeling Language
Authors: Haomin Wu , Haomin Li , Xinai Zhang , Miao Yu , jinze Song
Published: April 2021

This article is to show a new type of aircraft system design method, which decomposes the civil aircraft high-frequency data link system architecture into functional architecture, logical architecture and physical architecture, and describes the hierarchical relationship between the function, logic and physics of the system through the SysML model language.This article shows the method what converts the traditional high-frequency data link system design work based on experience and documents into model-based architecture design work.It uses the association and traceability of the model to ensure the integrity of the information in the design process and achieve the purpose of fast and convenient system design.
Qualitative Determination of Pesticide Residues in Purple Cabbage Based on Near Infrared Spectroscopy
Authors: Li Min , Lu Linju , Zhang Xiaoying
Published: April 2021

A qualitative detection model of pesticide residues based on near infrared spectroscopy and support vector machine (SVM) classification is proposed in this paper. The model was used to identify the pesticide free purple cabbage and pesticide containing purple cabbage. Cypermethrin, matrine and avermectin were selected respectively, and the ratio of pesticide and water was 1:100. The correct rates of classification and identification were 90%, 100% and 100%, respectively. The experimental results show that the model has high accuracy and universality for qualitative analysis of pesticide residues in purple cabbage. This paper presents an effective method for rapid and nondestructive detection of pesticide residues in vegetables.
Research on Manufacturing Readiness Level Improvement of Hybrid Integrated Circuit Based on Intelligent Manufacturing
Authors: Zhao Hailong , Zhang Yu , Zhang Kui , Huang Jie , Ran Honglei
Published: April 2021

This paper takes Hybrid Integrated Circuit (HIC) as research object, on the basis of fully understanding the concept and connotation, grading requirements and evaluation process of Manufacturing Readiness Level (MRL), aiming at the manufacturing risk factors and its sub factors in MRL, based on intelligent manufacturing technology, several ways to improve MRL of HIC are discussed, including intelligent upgrading of manufacturing equipment, intelligent management system, intelligent warehousing and logistics, intelligent interaction of Internet of Things (IOT), big data analysis and mining.
Evaluation of Normal Meshing Profile Deviation based on 3D Measurement
Authors: Zhao Baoya , Shi Zhaoyao , Yu Bo
Published: April 2021

The feature lines on involute surface include profile, helix, normal meshing profile and contact line. In helical gear transmission, the deviation of normal meshing tooth shape can reflect the transmission quality of gear better. In this paper, a method is proposed to obtain the tooth flank normal deviation from the 3D measuring point cloud data of gear, and to obtain the normal meshing profile deviation curve from it, and to evaluate it.
Research on Bionic Foot Design and Climbing Strategy of Quadruped Robot
Authors: Yan Tong , Su Bo , Xu Wei , Qiu Tianqi
Published: April 2021

Aiming at the problem that it is difficult for a quadruped robot to climb a large Angle slope, the bionics principle of goat climbing was analyzed. A bionic goat hoofs structure and a foot-to-ground coupling climbing model were designed. A climbing strategy of lateral sloping on a large slope was proposed, and foot trajectory planning under this model was studied. This design can assist the quadruped robot to climb the specific rugged surface with large slope, By building a single leg control model, combining with the Simulink and Simpack co-simulation technology, the single leg bench 60° slope simulation test, simulation results show that this design can effectively improve the quadruped bionic robot for rugged terrain with large slope stability of slope surface support.
Mechanism Design and Analysis of a Multi-DOF Flexible Ankle Rehabilitation Robot
Authors: Lu Yi , Zhang Xingfu , Ai Qingsong
Published: April 2021

According to the needs of global social development, this paper briefly describes the design and modelling of an ankle rehabilitation robot. A multi degree-of-freedom (DOF) ankle rehabilitation robot mechanism is constructed by using flexible pneumatic muscle actuators. The relevant mainstream control methods are analyzed. On this basis, we integrate the ankle rehabilitation robot with virtual reality technology and functional electrical stimulation technology, to further improve the participation of patients and improve the efficiency of ankle rehabilitation.
Regarding Informational Model Of Energy Management Systems
Authors: Lyakhomskii A , Petrochenkov A , Perfilieva E , Kutepov A
Published: April 2021

The relevance of the management of energy resources of industrial enterprises on the basis of the provisions and requirements of energy management systems with the integration of technical and management components of the energy consumption process with the unification of personnel on the basis of a single organizational and technological platform is considered. The characteristic of energy management systems consulting is given, the expediency of its implementation by specialized organizations is substantiated. To design the information model of Energy management system it’s necessary to identify and analyse the department structure, the whole list of the documents of the department and analysis of the routes of the department documents according to the research data.
Simulation of the static synchronous compensator for the system voltage unbalance and high current harmonics compensation from single phase non-linear load
Authors: Makarov A V , Makarova T V , Metelev I S
Published: April 2021

This paper presents the STATCOM model in PSIM, which compensate grid voltage unbalance and current harmonics from the non-linear, two-phase load. Grid voltage unbalance level and harmonics current of non-linear load given from measurement. The model of STATCOM is a 6-level voltage inverter. System voltage, and harmonics currents are controlled. All modes are realized and plots are plotted.
Research on Turbidity and Surface Hydrophobicity of Milk Protein at Different Conditions
Authors: Liu Zhenyan , Qin Lei
Published: April 2021

This study is mainly about determination of turbidity and surface hydrophobicity at different conditions. We use skim milk powder (SMP) as raw materials, the protein dispersion was prepared by dropping 1 wt% (protein basis) with the skim milk powder in Millipore water, and stirring at room temperature for 2 h. The samples were adjusted to different pH values (6.2 、 6.3 、 6.4 and 6.7) by stepwise addition of 0.1 mol/L HCl, then transferred to glass tubes and heated for 0-30 min at 80-100°C in a water bath. After the heat-treatment, the milk samples were immediately cooled to room temperature by immersion in an ice bath, and determining turbidity and surface hydrophobicity of milk protein at different conditions.
A Review of Target Identification Strategies for Drug Discovery: from Database to Machine-Based Methods
Authors: Shangguan Zehua
Published: April 2021

In recent years, target identification has become more efficient than before, and it helped to discover huge amounts of drugs for various diseases. The mystery buried behind was the methods that developed in recent years utilized in the target identification. The advances and research status of database, biological assay and machine-based method in recent years for target identification would be integrated by this review. The various databases help scientists to find information about target property, chemical property or on genome level. The biological assay, such as RNAi, RNA sequencing, DNA microarray, and Gal4/UAS system, is commonly used to identify the target in recent years. The machine-based strategies, such as random forest algorithm and Support Vector Machine (SVM) algorithm, could help scientists identify the target and find compound activity more efficiently. Among the three methods mentioned above, the machine-based methods could have higher efficiency and lower cost while maintaining higher accuracy. Despite the promising properties of machine-based methods, the combination use of biological assay would still be necessary. With wider application of more efficient strategies, target identification, as well as drug discovery process, would gain more rapid development.
Characterization of low-pressure MWPC from 1E3 to 1E5 Pa
Authors: Wang Y. , Sun Z. , Wang X. , Yi H. , Jiang W. , Fan R. , Zhou L. , He Y. , Ning C. , He Y. , Zhao Y. , Sun K. , Gao K.
Published: April 2021

The LPMWPC can be used as the Δ E detector for the low-energy charged particle detection. In order to increase the transmittance, the wires were adopted as the cathode. This work investigated the LPMWPC signal characteristics of this configuration and measured the gas gain with a mixture of 90%Ar and 10% CO2 from 1E3 to 1E5 Pa. From the test, the second pulse after the avalanche signal was observed, which proved to be caused by the ions' drifting near the cathode wire.
Air pollution and the noncommunicable disease prevention agenda: opportunities for public health and environmental science
Authors: Howse Eloise , Crane Melanie , Hanigan Ivan , Gunn Lucy , Crosland Paul , Ding Ding , Hensher Martin , Rychetnik Lucie
Published: April 2021

Air pollution is a major environmental risk factor and contributor to chronic, noncommunicable diseases (NCDs). However, most public health approaches to NCD prevention focus on behavioural and biomedical risk factors, rather than environmental risk factors such as air pollution. This article discusses the implications of such a focus. It then outlines the opportunities for those in public health and environmental science to work together across three key areas to address air pollution, NCDs and climate change: (a) acknowledging the shared drivers, including corporate determinants; (b) taking a ‘co-benefits’ approach to NCD prevention; and (c) expanding prevention research and evaluation methods through investing in systems thinking and intersectoral, cross-disciplinary collaborations.
Vehicle Dispatching Technology Based on Geographic Grid Division
Authors: Lei Zhengyi , Jiang Wenjing
Published: April 2021

With the increasing popularity of online ride platforms, more and more historical data can be used in the field of intelligent transportation. However, the taxi behavior of users is difficult to predict and has a great relationship with time and geographical location, which makes the supply and demand of vehicles show an imbalance. Reasonable vehicle scheduling can not only increase driver’s income, improve service quality, but also make effective use of traffic resources. In this paper, a grid-based scheduling model is proposed. By gridding maps, the computational complexity of Donger is reduced to output vehicle scheduling actions quickly. The problem of vehicle supply and demand imbalance can be effectively solved by correcting model parameters based on return loss function. In this paper, the model is optimized from three aspects: loss function, learning rate and grid scale. The goal is to maximize the sum of orders. Compared with the traditional machine learning model, the model proposed in this paper can have deeper features and thus obtain higher prediction accuracy. The experimental results on a large number of real historical taxi order datasets show that the proposed model has high prediction accuracy.
Research on the Importance and Application of Big Data to Equine Culture Communication
Authors: Ding Peng
Published: April 2021

China is an ancient civilization with a long history. However, in the long history, horses have played a key role in the production and life of the people, so later generations compare the performance evaluation to “sweat horse work”. From a certain point of view, the role played by horses in the formation and development of civilization is far more than that of human beings. Unlike other animals, the power, speed, and value of the horse have a positive effect on people’s daily lives. Since the domestication of the horse, it has contributed more profoundly to the development of China’s way of life and the process of social history and has also made remarkable contributions to the far-reaching development of ancient Chinese agriculture and industry. Therefore, for different ethnic groups in different regions, their roles in the production of the local people at that time were also varied, so each region gradually formed equine culture in line with its own regional development. Besides, the horse culture has also influenced other aspects of human beings. However, nowadays, with the continuous development of science and technology, the status of horses in human life or production is declining rapidly. The article introduces the connotation and characteristics of equine culture and big data, deeply analyzes the main relationship between big data and ethnic culture communication, the problems of equine culture communication in the current stage, and proposes strategies to solve them, so as to provide a reference for the communication of equine culture under the background of big data in the new era.
Mediating effect of motivation on employees’ performance in a private hospital, Indonesia
Authors: Fahlevi M
Published: April 2021

Quality human resources in a hospital is very important in producing excellent services, both medical personnel those are paramedics and non-medical personnel who work together in one unit that must be developed and maintained well in order to be able to produce excellent service and compete in welcoming the era of globalization. Increased incomes experienced by some people in Indonesia create new lifestyles that are far from health so that greater community vulnerability to disease and this is a new challenge for hospitals to improve services in the health sector. The research seeks to explain important factors that affect employee performance in a private hospital. The novelty of the research is to look for and compare the direct and indirect effects between factors that affect performance, especially the role of motivation as a mediating variable. The research uses 100 samples taken from employees of one private hospital in Indonesia. The result finds out that there is a significant influence between work environment, competence, and motivation on performance both directly and indirectly.