bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accurate Exercise Recommendation Based on Multidimension -al Feature Analysis
Authors: Zhang Shu , Cai Jiaqi , Zhuge Bin , Dong Ligang , Jiang Xian 
Published: June 2020

Abstract
With the rapid development of the Internet, online learning has developed rapidly, and learners are increasingly demanding the personalization and practicality of exercise. Facing the massive exercises in the online learning platform and the online examination system, how to choose the exercises that can be targeted and can make up for the knowledge loopholes has become a hot topic in the field of personalized recommendation of current teaching resources. In view of the fact that learners have a variety of learning features, and there are a large number of online exercises, various types and varying degrees of difficulty, this paper proposes a precise recommendation method based on Multidimensional features analysis for exercises. It quantifies the potential relationship between the learner and the exercise from three aspects: the heat of the exercise itself, the relevance of the knowledge among the exercises, and the similarity of the learner’s style, using the linear combination and the Learning to Rank method to build the recommendation model to match learner and exercises exactly. Experiments show that the mean average precision of the ReMFA method reaches 36.8% when recommending five candidate exercises, which can provide learners with personalized exercise recommendation services, thereby improving the learner’s learning efficiency.
Research on Network Isolate Method in Cloud Environment
Authors: Lai Xinming , Wang Haitao , Jing Zhao , Fan Zhang , Chao Zhao , Wu Gang 
Published: June 2020

Abstract
This paper proposes a network isolation method for large-scale cloud environments. The method run an Envoy-based endpoint program to proxy all network traffic on a physical node. And by configuring a route discovery registration center to uniformly maintain the network meta information of all virtual machines, the virtual machine uses the native VLAN technology to divide the user’s subnet to ensure the communication performance of the subnet nodes and the virtual machine network maintained by the route discovery center. Meta information improves the speed of virtual machine ARP addressing and improves cross-node network request performance. Envoy’s network proxy technology is used in conjunction with the route discovery center to broadcast the subnet broadcast communication of all virtual machines to the route discovery center, which improves the accuracy of the broadcast and greatly improves the utilization efficiency of the public network bandwidth.
Architecture of embedded intelligent video analysis system for forest fire prevention
Authors: Zhang Baiguo , Zhang Zhihao 
Published: June 2020

Abstract
Forest fire has always been an important hidden danger in forestry management. It is necessary to monitor and manage forest fire through video monitoring system. However, due to the lack of real-time performance of traditional video monitoring system and the difficulty in data processing, an embedded intelligent video analysis system is designed. The system uses a software and hardware design method based on Zynq SoC, first, each functional module of the hardware architecture is divided, then the system software is designed, and finally the system is evaluated as a whole. Based on the application requirements of forest fire prevention, this paper focuses on the detailed description of the underlying hardware architecture and operating system software design, and provides some value and reference for the application scenarios of forest fire prevention.
Estimation of triceps muscle strength based on Mechanomyography
Authors: Xie Jiazhi , Zhang Yue , Yang Ke , Xia Chunming 
Published: June 2020

Abstract
The aim of this study is to establish a reliable and widely applicable muscle strength (MS) estimation model based on the Mechanomyography (MMG). Seven healthy male volunteers were recruited to collect MMG and MS during the isometric contraction of their triceps. For MMG, 18 features were extracted. For the extreme gradient boosting (XGBoost) model and the quadratic polynomial (QP) model, the feature combination with the best estimation result was selected. The MS estimation performance of the XGBoost model and the QP model were compared. The performance of the QP model on the estimation of MS in different frequencies, different fatigue states and time periods was evaluated by using t-test. The results showed that when the number of features exceeds three, the model estimation accuracy has not improved significantly; and there was no significant difference in the estimation result of MS between the two models (p < 0.05), though the QP model was slightly better. The normalized root mean square error (NRMSE) and goodness of fit R of the MS estimation by the QP model were: 0.1343 ± 0.0296 and 0.8273 ± 0.0376. There was no significant difference in the MS estimation results in different conditions (p < 0.05).
Drive control strategy of two-wheel independent drive electric vehicle
Authors: Li Jiashun , Yang Yang , Xiong Yuhang 
Published: June 2020

Abstract
According to the advantages of two-wheel independent drive electric vehicle that the torque and rotational speed of the drive wheel can be easily measured, a driving control strategy of driving antiskid control and torque coordination control is proposed based on road surface identification slip rate and adhesion coefficient. The principle of road surface identification is given. According to the signals of wheel speed sensor, lateral acceleration, longitudinal acceleration and torque sensor, the wheel slip ratio, road surface adhesion coefficient, vertical force and longitudinal force are calculated. Combined with μ-S function curve proposed by Burckhardt et al., the road surface adhesion coefficient and optimal slip ratio are identified. Driving antiskid control strategy adopts PID control technology based on road surface identification of optimal slip rate, peak adhesion coefficient and optimal slip rate, which mainly includes four parts: establishment of wheel stress model of two-wheel independently driven electric vehicle, approximate calculation of slip rate and road adhesion coefficient, optimal slip rate estimation based on fuzzy control and PID controller design based on optimal slip rate. The torque coordination control adopts PID control method based on ideal yaw rate.
Research on three-phase four-wire active power filter based on LCLCL coupling structure
Authors: Hu Jinbo , Liang Jiajian , Ding Yizhou , Ding Ankang , Yin Zhanqing , Li Zirui 
Published: June 2020

Abstract
In this paper, the main circuit topology of the three-phase four-wire active power filter (APF) and its harmonic current detection and control algorithm are studied. An active power filter system combining LCLCL filter and a three-phase H-bridge structure is proposed. This paper has applied harmonic detection algorithm based on an improved synchronous phase-locked loop (SRF-PLL) and recursive discrete Fourier transform (RDFT) while using triangular carrier modulation control based on PI adjustment to realize the harmonic compensation of the power filter to the grid current. The system has a notch effect on the harmonic at the switching frequency. It can detect the fundamental component and each harmonic component of the load current separately with a small amount of calculation, good real-time performance. Finally, a 5KVA single-phase APF prototype was built to test the superiority of the main circuit topology and the algorithm of current detection and control.
Firewall Technology in Computer Network Security in 5G Environment
Authors: Song Xinghua 
Published: June 2020

Abstract
in the Process of Computer Network Security Maintenance, Firewall Technology is the Main Security Maintenance Approach and Can Play an Effective Protection Role. Due to the Popularity and Widespread Use of Computer Technology, Network Security Has Received Widespread Attention from All Walks of Life. the Purpose of This Article is to Study Firewall Technology in Computer Network Security Based on 5G Environment. 5G Mobile Networks Also Have Some Shortcomings, Such as Weak Network Penetration Capabilities. Once the Base Station is Not Fully Deployed, There Will Be Gaps in the Borders of Communication Cells, Resulting in Network Data Transmission Interruption. in Order to Solve the Above Problems, This Paper Proposes Mobile Network Optimization Measures. Based on Many Years of Practice and Exploration, It Focuses on Describing the Complex Communication Application Scenarios in Cities, and Introduces a Variety of Mobile Network Optimization Technologies to Achieve Seamless Network Coverage and Ensure That People Can Use Mobile Communication Networks Normally. the High-Speed, Large-Capacity, Low-Latency, and High-Reliability Services Brought by 5G Will Have an Impact on the Local Optical Cable Architecture and Core Utilization. According to the Experimental Results, the Effective Application of Firewall Technology Can Greatly Improve the Security of Computer Networks and Ensure the Security of Computer Networks to a Great Extent.
Image Raindrop Removal Method for Generative Adversarial Network Based on Difference Learning
Authors: Chen Renhe , Lai Zhenyi , Qian Yurong 
Published: June 2020

Abstract
Due to the interference of the external environment such as rainy weather on the camera, raindrops can easily adhere to the lens and seriously affect the quality of the photos taken. Therefore, it is of great significance to remove raindrops from the image and improve the quality of the photo. In this paper, a raindrop method for generative adversarial network images based on differential learning is proposed. The general generative network is to input images with raindrops and output clean images. The generative network in this paper does not directly output clean images, but learning the difference between images with raindrops and without raindrops, then subtract the learned difference from the image with raindrops to generate a clean image. In order to learn this difference more effectively, adding reconstruction loss to the generative network, the pre-trained VGG-16 network is used to extract the difference between the generated image and the real image features and calculate the mean square error. The experimental results show, the method in this paper can not only remove the raindrops in the image well, but also reconstruct the image information of the part blocked by the raindrops. The image processed by the algorithm in this paper is tested using the yolov3 target detection algorithm, which can significantly improve the recognition accuracy of the detection algorithm.
Accurate road segmentation in remote sensing images using dense residual learning and improved focal loss
Authors: Ma Junjun , Xu Zhaohong , Zheng Ergong , Fan Qiongjian 
Published: June 2020

Abstract
In this paper, we propose a road segmentation model using deep learning technology. The model is essentially a U-net with pre-trained DenseNet-169 encoder. Multi-scale and high-level semantic information are extracted effectively by dense residual learning and attention mechanism. In addition, an improved focal loss function is proposed to handle extremely imbalanced road samples. Experimental results demonstrate that our proposed road segmentation model can accurately extract complex road areas in remote sensing images and has IoU of 0.6308 which ranks 22st in the DeepGlobe Road Extraction Challenge leaderboard at the time of writing.
Design of monitoring terminal for SAS based on flexible fabric electrode
Authors: Zhang Shuo , Chang Zhiqiang , Zhang Xiaofeng , Ge Wei , Ji Xiaoqiang 
Published: June 2020

Abstract
Sleep apnea syndrome is a serious threat to the life and health of patients. In this paper, we designed a sleep apnea monitoring system based on flexible fabric electrode, and completed the specific hardware design including flexible fabric electrode, ECG signal conditioning module, microcontroller module and Bluetooth module. Among them, ADS1292 is mainly used for ECG signal conditioning, and EFM32 is selected as microcontroller. Finally, the experimental results show that the collected signal can clearly identify the ECG characteristics such as QRS wave, and meet the requirements of the subsequent research of ECG signal. The terminal equipment we designed is low cost, comfortable to wear, and can be monitored for a long time, which is suitable for daily household monitoring.
A Method of Describing Uncertain Emotions of Facial Expressions
Authors: Lin Junhuan , Chen Yuefen 
Published: June 2020

Abstract
In order to describe uncertain and fuzzy emotions in recognizing facial expressions, a PAD regressive model based on deep convolutional neural network model is built to quantize the emotions, by which the facial expressions can be mapped to the emotional space of PAD. Then the emotional membership function is proposed to describe the uncertainty and fuzziness of the emotions in the space of PAD. Finally the testing results demonstrate that the regressive model performs well in recognizing facial expressions and the results from describing the emotions quantitatively agree with the human cognition to facial expressions.
Real-time detection of UAV detection image of power line insulator bursting based on YOLOV3
Authors: Chen Songbo , Su Chao , Kuang Zhenxing , Ye Ouyang , Gong Xiang 
Published: June 2020

Abstract
The insulator fault of high voltage line is the main factor of transmission accidents, so positioning and detection of burst insulator of power line has become an important part of routine detection. Traditional UAV detection is still mainly to evaluate the image transmitted by UAV manually, which is not only time-consuming, but also not accurate. In this paper, UAV aerial images are used to make datasets, the problem of insulator class imbalance (normal insulator and burst insulator) in the model training process is solved by using data augmentation, an improved depth learning algorithm is proposed based on YOLOv3, which provides rich semantic information for prediction layer by adding feature mapping module. At the same time, the residual network is introduced into the feature extraction, which improves the detection accuracy of small objects, more effectively extracts the object features of burst insulator, carries out real-time object detection and positioning, and completes the daily detection of insulator state of power transmission lines. The experimental results show that the detection accuracy (mAP) of the improved YOLOv3 algorithm is 91.22%, and the detection speed is 28 frames/s. The recall rate and Intersection Over Union (IOU) are also improved.
3D Point Cloud Surface Reconstruction Based on Divide-and-Conquer Method in Laser Scanner
Authors: Lyu Yuan-Zhi , Guo Min , Sha Ou , Zhang Hong-Yu 
Published: June 2020

Abstract
As to the problem of surface reconstruction in 3D laser scanner, we propose a divide-and-conquer based method, and realize the continuing growth of the reconstructed surface through comprehensive analysis of the correlations of scanning line point cloud data. First of all, filter the scanning line point cloud data, then confirm the relationship between the newly-insert point and the generated surface by using the local search algorithm based on adaptive point cloud segmentation, and finally, classify the newly-inserted points as sparse points, density points, growth points and flyback points by using the given maximum and minimum distance thresholds, and then the specific surface reconstruction is performed according to the point’s classification. The experimental results show that the local search algorithm greatly reduces the calculation of the insert location of the point cloud compared with the global traversal search algorithm; the proposed method can process the flyback points, enhancing the repairability and the expandability of the reconstructed 3D surface compared with the traditional method. Furthermore, the quality of the reconstructed 3D surface, the generating speed and the occupation of storage space can all be controlled by adjusting the maximum and minimum distance thresholds according to actual demand.
Research and Application of Multiscale Representation and Regularization Method in Image Recognition
Authors: Heng Wang , Zheng Bi-Geng 
Published: June 2020

Abstract
Image Recognition is the Use of Computers to Process, Analyze and Understand Images to Identify Different Patterns of Objects and Images. in This Paper, We Introduce the Idea of Nonlocal Processing on the Basis of Regularization Tv. the Process of Image Recognition System is Divided into Five Parts: Image Input, Preprocessing, Feature Extraction, Classification and Matching, in Which Preprocessing Can Be Divided into Image Segmentation, Image Enhancement, Two Value Reduction and Refinement and So on. the Paper Presents Research and Application of Multiscale Representation and Regularization Method in Image Recognition. At the Same Time, through Programming in the Matlab Programming Environment to Achieve the Relevant Algorithm, through the Actual Image Recognition Experiments, the Use of These Conclusions, and Finally to Accurately Identify the Results.
Research on Short-term Traffic Flow Forecast and Auxiliary Guidance Based on Artificial Intelligence Theory
Authors: Wang Rongxia 
Published: June 2020

Abstract
With the development of social economy, intelligent transportation system (ITS) has been booming. It is mainly to realize the omni-directional, real-time, accurate and efficient guidance and control of transportation in a large range. Traffic flow prediction, especially short-term traffic flow prediction, is the basis of urban traffic control and guidance, so traffic flow prediction system is one of the important subsystems in its. Short-term traffic flow prediction technology belongs to the important research field of intelligent traffic control and vehicle guidance. It can help cities to conduct intelligent traffic guidance and enable users to choose the optimal path. Prediction of road traffic flow state and auxiliary guidance are very important basic theories. Based on the theory of artificial intelligence, this paper analyzes the short-term traffic flow prediction and auxiliary induction methods, points out the limitations of the existing prediction methods, and proposes a short-term traffic flow intelligent prediction method combined with artificial intelligence technology.
Development and Trend of Space-based Information Network
Authors: Liu Zhenhua , Lin Chuanwen , Chen Gang 
Published: June 2020

Abstract
With the development of space-based technology, the construction of the space-based network has been into a period of major change, countries are putting forward their own space-based network and getting them off the ground. This paper analyzes the space-based network development situation, introducing the SCaN network in the United States, Europe ISICOM plan and the development of China’s SGIN, discussing the trend of the development of the space-based information network. At present, the construction of space-based network is still based on the foundation network as the core, the space-based system as the expansion, based on the communication interruption satellite, gradually evolves into the space-based network, in the future it will integrate the space-based navigation, space-based information acquisition to form a space-based integrated information network.
Comprehensive Evaluation Method for Network Node Importance based on Constructed Tree
Authors: Yang Yuxin , Peng Yali , Huang Minghe 
Published: June 2020

Abstract
The importance measurement of nodes in directed weighted networks is of great significance for theoretical research and practical application of networks. Aiming at the directed weighted network model in actual networks, based on the special transformation of graphs and tree roots, this paper proposes a comprehensive quantitative assessment method for the importance index of nodes based on the directed weighted network’s global properties, local attributes, network location and bidirectionally propagation. This method transforms the nodes to be analyzed in the graph to root, constructs a bidirectional depth-affected XD tree and a breadth-affected XB tree with the node to be evaluated as the root of the tree. Then comprehensively consider the two-way influence of the node level, node neighbors and the closeness between neighbors in the two trees constructed, and the in-out effects, build a deep impact evaluation model at the two-way level, two-way neighbor information and comprehensive evaluation index of clustering coefficient. By calculating the comprehensive importance value of each node in the network, the comprehensive ranking of the importance of the nodes in the network can finally be obtained. The robustness experiment of ARPA network shows that the G value of the important node quantified by this method has absolute advantages, this method can effectively analyze key nodes in the network.
The Analysis on Student’ Psychologic Status of Online Learning under Extraction Model from Computer Face Features
Authors: Wen Lan , Yang Shaogang , Zeng Jiao , Liang Xiaowen , Xu Yanping 
Published: June 2020

Abstract
The research aims to analyze students’ psychological mood during online study and enhance the efficiency of learning. In this research, face features are extracted by LBP (local binary pattern) algorithm based on face feature recognition. The facial feature points are tracked by CLM (constrained local model) algorithm, and the face images are normalized by AdaBoost algorithm. In the end, the fatigue level of learners is calculated by P80 method in PERCLOS criterion to verify the accuracy, recognition rate and error rate of the model. The results indicate that through the recognition of the faces in a complex background, the accuracy of the mode is 95%, the recognition rate is 95.53% and the error rate is only 0.53%, the range of aspect ratio in the human eye image is 0.22 ≤ λopen ≤ 0.27, 0.05 ≤ λclose ≤0.1, and learner’s level of excitement can be estimated according to the range of f. Therefore, this algorithm model can extract learner’s facial features very well and show a good result. Detecting the degree of learner’s excitement accurately can provide an important fundamental for the realization of online learning emotion detection system which also has guiding significance for the development and popularization of the networked education.
Minimizing brain drain: how BumDes holds the best resources in the villages
Authors: Asmuni  , Rohim  , Trihartono A. 
Published: June 2020

Abstract
This paper examines the extent the BumDesor Village-Owned Enterprises (hereafter BumDes) may minimize the local Brain drain in the village. Brain drain is a severe problem in Indonesia as they affected directly to the development of the village. Brain drain leads to the absence of human resources in the village, causing the best people to leave the village. Using qualitative methods and in-depth interviews in three villages in East Java, in Malang District, Banyuwangi District, Gresik District, this work found that the BumDes counter the brain drain to some extent. This paper demonstrates that there are three patterns of how brain drain can be minimized through the BumDes. Those are women’s empowerment in BumDes business units, empowering village youth to manage the digitalization of the BumDes business unit, and recruitment of local fresh graduated from college to run the BumDes. In short, the BumDes could keep the best human resources to develop the villages.