bluuebunny/crossref_metadata_2025 huggingface dataset:
A collaborative filtering recommendation algorithm based on user clustering with preference types
Authors: Yang Yan , Yao Huaxiong , Li Rong , Wang Sai
Published: April 2021

The traditional collaborative filtering algorithm is extensively applied in the field of personalized recommendation, but it still faces the challenges of data sparsity and scalability problems which result in low quality and efficiency of recommendation. To address the problems, a hybrid collaborative filtering recommendation algorithm is proposed based on user preference type clustering. First, by analyzing the relationship between users and item categories, we construct the user item category preference matrix. On this basis, user clustering is carried out and users with similar preference types are clustered into the same user groups. Then, to search for the nearest neighbors of target user, similarity of users is calculated in the cluster it belongs to by considering both the user ratings and their preferred item categories. Finally, an improved Slope One algorithm is proposed and applied to the nearest neighbor set to predict item ratings and then generate recommendations. The experiments indicate the proposed hybrid collaborative filtering algorithm can improve recommendation performance in the case of a great many users.
Research on chaotic flying sparrow search algorithm
Authors: Chen Xiaoxiao , Huang Xueyu , Zhu Donglin , Qiu Yaxian
Published: April 2021

The sparrow search algorithm has attracted much attention due to its excellent characteristics, but it still has shortcomings such as falling into the local optimum and relying on the initial population stage. In order to improve these shortcomings, the chaotic flying sparrow search algorithm is proposed. In the initialization, the chaotic mapping based on random variables is introduced to make the population distribution more uniform and speed up the optimization efficiency of the population. In the discoverer stage, the dynamic adaptive search strategy and levy flight mechanism are used to increase the search range and flexibility, and the random walk strategy is introduced to make the follower’s search more detailed and avoid premature phenomenon. The effectiveness of the improved algorithm is verified by six standard test functions, and the introduction of a variety of strategies greatly enhances the optimization ability of the algorithm.
HCHODetector: Formaldehyde concentration detection based on deep learning
Authors: Cao Zhihao , Shao Mingfeng , Shi Aiju , Qu Hongchun
Published: April 2021

Currently, deep learning technology is developing rapidly. Deep learning is mainly used in the fields of vision and hearing for human beings, but less in the field of olfactory. Formaldehyde is a common gas harmful to human health. However, the traditional methods of Formaldehyde concentration detection are inefficient in some cases. As for this problem, this paper proposes a novel formaldehyde detector namely HCHODetector. Specifically, this detector is based on deep learning and HSV colour space augmentation. Moreover, we propose a novel Mask-guided module and a novel pre-training network to enhance the colour discrimination ability of HCHODetector. As a consequence, the experimental results show that the detection error is within 0.08 mg/m3 in the actual environment, which provides a new idea for Formaldehyde concentration detection.
Power patent classification method based on deep neural network
Authors: Min Hua
Published: April 2021

Aiming at the problems in power patent classification, a power patent classification method based on deep neural network is proposed. Firstly, the power patent is analyzed for preprocessing. Then, the key words in the patent abstract are extracted to construct a power vocabulary dictionary. Then, the power patent classification model is built according to the deep neural network. Finally, the power patent classification model based on deep neural network is used to classify the power patents, and the accuracy is close to 98%. It can be seen that the deep neural network method can not only realize the classification of power patent, but also has good classification effect.
Parameter Anomaly Identification Model About Supervisory Control And Data Acquisition
Authors: Ping Guo
Published: April 2021

With the rapid progress of sensor technology, state monitoring of wind turbines is more comprehensive, the amount and type of the sensor used will be more, the state parameters of wind turbines will be more. According to the status indicators to parameter index of target parameters selector model is established in this paper, and the number of input parameters are reduced on the basis of the guarantee accuracy. The two sub-models constitute the abnormal identification model of the working condition index. Finally, the model is analyzed by an example to verify the accuracy and effectiveness of the model. The research in this paper on abnormal discrimination of working condition parameters and evaluation methods of wind turbines is an important premise and basis for making scientific and reasonable operation and maintenance decisions of wind farms, and it has important academic significance.
Applications of Markov Chain in Forecast
Authors: Yutong Xia
Published: April 2021

The article is going to introduce Markov chain and its application into business. Two main daily life examples are applied for the explanation of this theory. In the Markov process, the probability of one state only depends on the next previous state. A mathematical model of market forecast and weather forecast is able to build through constructing transition probability matrix, analysising and computing with Markov chain. According to the data and calculation verification, we can see Markov Chain is a scientific, effective and convenient method for prediction and can be used for solving daily issues.
Research on watermark printing technology of STL model based on Menger curvature
Authors: Zhang Changhao , Li Hu , Chen Xianggang , Shi Xiaolei
Published: April 2021

In order to solve the data protection problem of STL model, this paper proposes a 3D printing watermarking method based on Menger curvature and K-means clustering algorithm. By comparing the research contents of 3D mesh model watermarking algorithm and two 3D printing watermarking methods, the 3D printing watermarking method is applied based on Menger curvature and K-means clustering algorithm. The method flow, watermark embedding and watermark extraction are discussed. Finally, the experimental results show that this method can not only improve the invisibility of model watermark, but also be robust to geometric attacks such as rotation, scaling and translation.
Research on marginal cost model of electric vehicle charging and swapping facilities based on queuing theory
Authors: Hu Lanxin , Yue Tyler
Published: April 2021

Research on the configuration of charging and switching facilities for electric vehicles is an important part of the development of related industries. In this paper, based on the analysis of charging behaviour of electric vehicles, the mathematical model is established by using queuing theory, and on this basis, the marginal cost theory of microeconomics is introduced to analyse the number of charging facilities under different utilization rates. The marginal cost model of charging and switching facilities is established with the objective of minimizing the total marginal cost of charging and switching facilities service system, and is verified by an actual example. The results show that through the rational allocation of the number of facilities, the marginal cost under different utilization rates can be optimized, which can provide strong support for decision makers to make operation plans and promote industrial development.
Space Robot Target Intelligent Capture System Based on Deep Reinforcement Learning Model
Authors: Liang Binyan , Chen Zhihong , Guo Meishan , Wang Yao , Wang Yanbo
Published: April 2021

There are many on-orbit capture tasks for space robots. At present, most space robots capture methods are based on the trajectory planning of robot kinematics. This kind of method has poor control precision in space environment. The intelligence degree of robot capture task is very low. We built a simulation environment for robot space target capture task based on physics engine. A real-time online simulation training platform is established in the simulation environment. We design a robot deep reinforcement learning motion control model based on Actor-Critic algorithm. We optimize the reward function of the DRL model. The reward function shortens the training time and improves the score performance of the model. The experiment data show that the DRL model converges in 800 steps. The average score and standard deviation of the model indicate that the model has successful completed the capture task of space robot.
Image Acquisition Time Series Prediction Method Based on Deep Learning
Authors: Mo Zhisong , Li Yifei
Published: April 2021

At present, in-depth learning can be said to be a learning method based on neural network, and time series can be used to achieve prediction results. Therefore, a time series prediction method for image collection based on deep learning is proposed. Based on time series prediction, the image collection in deep learning is analyzed, and the DBN model is combined with GCRBM model to train the model, identify the time series category, and reconstruct the sequence in order to achieve complete time series prediction. The experimental results show that the time series prediction method works well.
Application of Mathematical Model Based on Optimization Theory and Particle Swarm Algorithm in Radar Station Layout Optimization
Authors: Xuefeng Wang , Chen Mimi
Published: April 2021

The optimization problem of radar station layout was derived through its mathematical description to explore the application of the optimization theory and particle swarm optimization (PSO) in this optimization problem; after the optimization problem was solved, a disturbance factor was introduced to present the improved PSO algorithm (PSO-DF); in the end, the PSO-DF algorithm was used to construct an radar station layout optimization model. The simulation results of the algorithm performance and the radar station layout optimization model show that in comparison with the basic PSO algorithm, the improved PSO-DF algorithm has better local search performance; corresponding to different classical functions, the optimal value, the worst value, mean value and variance of the PSO-DF algorithm are all superior to those of the basic PSO algorithm; the fitness value corresponding to the simulation via the constructed radar station layout optimization model is about 0.8, and the network shows satisfying effects in the monitoring of different areas of responsibility. This research provides a certain reference for the application of the optimization theory and PSO algorithm in the field of radar station layout optimization.
Research on 3D Object Detection Method Based on Convolutional Attention Mechanism
Authors: Yong Zhang , Xiaoxia Zhang , Nana Da
Published: April 2021

In 3D object detection, the illumination and occlusion of the input point cloud data lead to inaccurate feature extraction, and the maximum pooling method destroys the information structure of the point cloud, leading to the problem of weak local feature expression. This paper proposes a 3D object detection method based on Convolutional Attention Mechanism (CAM). CAM first adds an attention mechanism to the first and last layers of the traditional feature extraction network structure, then fuses the feature information of different layers, and finally performs normalization operations. Experimental results show that this method has achieved better results on the KITTI and SUN-RGB data sets compared with mainstream algorithms DoBEM, MV3D, DSS, COG, 2D-driven, and FPNet. The mAP index has increased by 0.6%-12.5%. While CAM realizes the fusion of local and global information, it significantly improves the accuracy of object detection in illuminated and occluded scenes.
An improved U-Net method with High-resolution Feature Maps for Retinal Blood Vessel Segmentation
Authors: Dongye Chang-lei , Ma Yanyan
Published: April 2021

The accurate segmentation of retinal blood vessels is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. However, manual segmentation of retinal blood vessels is time-consuming and laborious. This paper proposes a convolutional network structure based on U-Net for retinal vessel segmentation. First, a new convolution block, which makes full use of shallow high-resolution feature maps to minimize the information loss caused by downsampling, is added to the network. Second, the network was downscaled. Particularly, this network application conducts downsampling twice to reduce the complexity of the network and the number of parameters during training. In addition, we retain the original short connection, which merges the feature information of the shallow and deep networks. Therefore, this network can capture the details of blood vessels more effectively. We tested the work on the DRIVE data set and evaluated the accuracy, sensitivity, specificity and AUC, which were 0.9552, 0.7603, 0.9839, and 0.9789, respectively. A comprehensive comparison between the proposed algorithm and the existing algorithms shows that the various indicators of the algorithm perform well.
Research on the Fusion Model of Sports and Medicine Based on Artificial Neural Network Health Analysis and Forecast — —Take atherosclerosis as an example
Authors: Yu Wang , Jinghong Wang , Pan Deng , Jinying Li , Yiting Wang
Published: April 2021

Purpose: With the increasing application of artificial neural network technology in medical diagnosis and health risk prediction, the model of sports medicine integration based on ANNs health risk prediction will provide an operable new idea for the development of sports medicine integration in China. Method: This study takes the more common atherosclerosis as an example and uses the artificial neural network analysis technology to predict the disease risk of 1109 patients, explores the effect of physical activity in predicting models, and explores new ideas for the integration of sports and medicine. Results: The model based on the LSTM network has a good predictive effect in realizing the level of physical activity and the risk of atherosclerosis. Therefore, it is recommended to include indicators such as physical activity level and exercise habits to predict and manage disease risks in the development of the integration of sports and medicine.
Discussion on Computer Network Security Prevention Based on Big Data Era
Authors: Sun Lixian
Published: April 2021

With the continuous development of reform and opening up and the continuous development of science and technology, information and communication industry has also achieved rapid progress. Certain problems will also be encountered in the development of computer technology. The most important problem is about computer network security. Some people are likely to be attacked by viruses and Trojan horses while using computers, leading to a lot of core damaging information. Some network hackers may also use computer loopholes to steal relevant information. Strengthening computer network security management is very important. This article will focus on computer network security in the era of big data as the main topic and discuss the relevant content.
Data Sensitivity Measurement and Classification Model of Power IOT based on Information Entropy and BP Neural Network
Authors: Zhang Qianyi , Zhang Chi , Ni Jiaming , Wang Xuqiang , Zhang Yao
Published: April 2021

For the problems of privacy data protection caused by massive data sharing in the construction of power Internet of things, a data sensitivity measurement and classification model based on information entropy and BP neural network is proposed. Firstly, a recognition matching algorithm is proposed to identify the sensitive level of attributes in the dataset, and the information entropy is used to determine the weight of attributes sensitivity level, so as to calculate the sensitivity measurement value of records in the dataset; finally, the BP neural network is used to output the data classification results. The experimental results show that the model can achieve accurate measurement and classification of data, with low incorrect judgment rate and error rate.
End-to-end drug entity recognition and adverse effect relation extraction via principal neighbourhood aggregation network
Authors: Kong Luyue , Lai Qinghan , Liu Song
Published: April 2021

Drug entity and adverse effect relation extraction is a critical task that aims to recognize drug entity and extract adverse effect relation from the unstructured medical text. Many works have used statistical learning approach, traditional or new deep learning approach to solve similar pharmacovigilance problem. Recent works tended to employ the graph convolutional network to enhance the ability of drug and adverse effect information extraction. However, the injective problem generated by a single aggregator and the weak generalization of the summation aggregator cause the graph neural network lack the ability to extract sufficient relation information. To solve these problems, we propose a new end-to-end model named recognizing Drug entities and extracting Adverse effect relations via the Principal Neighbourhood Aggregation network (DAPNA). Moreover, we compared the DAPNA with baseline models on the Adverse Drug Effect (ADE) dataset using multiple metrics. The experimental results demonstrate the method proposed in this paper achieves state-of-the-art results and can be applied to other drug and adverse effect information extraction tasks.
Human Error Evaluation Model of Civil Aviation Maintenance Based on Grey Wolf Optimizer
Authors: Ma Ying , Wang Rui
Published: April 2021

Today, with the rapid development of science and technology, human error has already surpassed mechanical failure and become the most important factor in safety accidents. In order to reduce the occurrence of human error in civil aviation maintenance, and to improve the quality of maintenance production, four levels and 18 factors affecting human error in civil aviation maintenance are proposed in this paper,Taking Hongqiao base of Eastern Airlines as an example, a questionnaire survey was used to collect data,and an Inertial Adaptive Hybrid Gray Wolf Optimization (IAHGWO) was proposed,which combined with particle swarm optimization (PSO) and three improved strategies,and an evaluation model was constructed to use IAHGWO to train BP neural network. The results show that the evaluation model has good practicability and accuracy.
One-Shot Image Segmentation with U-Net
Authors: Zhao Guanyi , Zhao He
Published: April 2021

Image segmentation is an important task in the field of computer vision research. Many applications need accurate and efficient segmentation mechanisms. However, most existing methods need a large support set and have struggled with dealing with new classes in solving computer vision problems. In order to deal with the problem, this paper modified the traditional model to adapt it to the task of few-shot segmentation. Especially, the part of encoders was changed into a Siamese neural network for support branch and query branch. Then, the decoded query image is compared with different levels of features from the encoded support image. Experimental results show that this proposed model achieved the best performance compared with several baseline methods, and the improvement is more than 8%.
Fault Diagnosis Method of Power Transformer Based on Improved PNN
Authors: Tang Diankui
Published: April 2021

Power transformer is a core hub of power transmission in power grid, and its operation state will affect the effective operation of power grid. The existing power transformer fault diagnosis methods mainly use the traditional dissolved gas analysis (DGA) method combined with artificial neural network. Probabilistic neural network (PNN) has a wide range of applications in the field of power transformer fault diagnosis because of its good fault tolerance and high training efficiency. In PNN, the smoothing factor has a great influence on the output, but the parameter is lack of efficient selection method, which makes the classification efficiency of PNN not high. Aiming at this problem, a diagnosis method based on improved PNN is proposed for power transformer faults in this paper. Bat algorithm is used to optimize the smoothing factor of PNN to determine the optimal value. Based on the optimal results, the network model is trained to obtain the optimal power transformer fault diagnosis model. The experiment fully proves that the improved PNN has higher diagnosis accuracy than other methods.
Analysis on influencing factors of X-shape dress silhouette based on eye-tracking technology
Authors: Nie Qiu Xia , Li Yue , Wang Xiao Gang
Published: April 2021

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Our times are developing rapidly. Because of the rapid development of computer technology, so led to the development of the times. So, in school all kinds of big and small things are basically replaced with computer systems to deal with. But it also has the effect that the data is redundant, so we need to use data mining algorithms to get the data we need. Therefore, the purpose of this paper is to use data mining algorithm, to build a data detection system for business administration disciplines, and to evaluate and simulate its experiments. So, we effectively extract the reasonable data we need based on the security of all kinds of data, to package it and aggregate analysis. Finally, after investigation to measure the accuracy of this system. Experimental results show that the system can greatly reduce the workload of the teaching system, and accurately complete the work, compared with the previous speed of about 120% faster and more accurate, by the teachers and students alike.
Data Mining Technology in Business Data Analysis
Authors: Qing Haohua , Zhang Jiali , Fu Die
Published: April 2021

With the vigorous development of e-commerce, people pay more and more attention to data mining technology. This research mainly discusses the application of data mining technology in business data analysis. This study uses the clustering algorithm of buyers’ purchasing behavior as an example to illustrate the definition of VMML in the clustering analysis of purchasing behavior. In this piece of XML, through the integration of data, an e-commerce research data warehouse is constructed, which is also used as the source data of EBizDAS. A certain amount of research has been done on buyers, sellers, commodities and transaction data, and the results obtained by EBizDAS analysis are displayed on the C2C e-commerce data analysis web platform for C2C users to browse and reference online. In this study, the 10 queries in CoHadoop took a total of 849 seconds, while the distributed strategy took a total of 748 seconds, with a performance improvement of about 12%, and the overall performance improvement was still considerable. This research has positive significance for the development of e-commerce.
Application of Style Transfer Algorithm in the Design of Animation Pattern Special Effect
Authors: Fan Xinxin
Published: April 2021

In recent years, the in-depth study and rapid development of computer vision technology has become an important aspect of image style transfer technology. Use the style transfer algorithm to complete the conversion of the artistic style of the image, and use computer tools to automatically convert the style of other images to the input image, so that the two styles are exactly the same, but the original image will remain unchanged, and this style will be overwritten in other images. In order to complete the reconstruction process, while protecting the original content of the image, there are other style transfer algorithms for visual effects. Computer vision, film and television production and other fields play an unusual role. The style transfer algorithm transfers the painting elements to the movie, and produces a unique animation special effect art effect, thereby providing new ideas and situations for the animation pattern special effect design of the artificial intelligence pattern style transfer algorithm. Therefore, exploring the theoretical approach of pattern style transfer algorithm and improving the diversity of pattern style transfer has greater trade value and industrial application value. Based on the deep learning theory, this paper describes the training model and deep learning framework of the convolutional neural network model VGG19, and introduces the basic technology in the application process in detail, and then applies the animation pattern special effect style transfer algorithm. Summarize the advantages of the style transfer algorithm in the design of animation pattern special effects.
New Active Defense Technology under the Background of Power Information Network Security
Authors: Li Jing , Huang Jie , Tian Li , Wang Jie
Published: April 2021

With the rapid development of the national power industry, the security of power information network has been widely concerned by the society, and the related impact has also been prominent. At this stage, the development of power information technology is more and more inseparable from the network, so network security has become more and more important, if the network has a serious threat in practical application, the loss caused will be unbearable. In the process of controlling the network security of power information industry, one of the most important and effective methods is active defense. Based on this, this paper puts forward the research of active defense technology under the background of power information network security, expounds the defects of firewall technology and intrusion trap technology in passive network security defense technology, and introduces the pitfalls of active defense new technology In addition, the paper discusses the new active defense technology and its application in power information network security, and puts forward reasonable suggestions. Therefore, the use of information technology to solve the hidden danger of power network security is important.