bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multifaceted sentiment analysis of public comments on the Dianping.com
Authors: Sui Liying , Shang Lu , Guo Xiaochao , Zhang Dexue 
Published: June 2020

Abstract
Emotional analysis of text has always been a hot topic in natural language research. In view of the long-term dependence of recurrent neural networks and the fact that most models do not consider the correlation between input and output, this paper proposes a bidirectional LSTM model based on attention mechanism to judge the comment emotion. This method vectorizes the semantics of comments into the LSTM model, improves the relevance of input and output through the attention mechanism, fuses the aspect category and aspect Term, and outputs the results through the classifier. The experimental results of Dianping.com review data set provided by AI Challenger competition show that the improved method adopted in this paper is better than the common deep learning method.
Cycle Fusion Network for Multi-Person Pose Estimation
Authors: Wang Yuanxiang , Wang Teng 
Published: June 2020

Abstract
Multi-person pose estimation has been largely improved with the development of convolutional neural network, which however remains a challenging task in computer vision. The challenge is particularly pronounced in high flexibility of occlusion, camera angles, imaging-caused body limbs. Existing methods are mostly based on networks with deep architectures, which are capable of inferring the semantic labels with high accuracy while suffering from positioning accuracy degeneration caused by the loss of the spatial information. To address these problems, we present a novel network called cycle fusion network to improve the pose estimation accuracy in multi-person environments. The cycle fusion network has a two-stage structure. In the first stage, a cycle-path feature fusion is used to increase the semantic information for low level layers and the spatial information for high level layers respectively. In the second stage, a cross-scale feature fusion is used for refining the positions of keypoints. Equipped together with RFB module and multi-level supervision, the proposed method has shown the state-of-the-art performance on the large-scale MS COCO benchmark with extensive experiments.
Analysis and simulation of rainfall clutter of primary surveillance radar for civil aviation Air Traffic Control
Authors: Zhang Huaicai , Yang Yuhang , Du Hui , Luo Qiming , Zhang Ye 
Published: June 2020

Abstract
To evaluate the influence of rain clutter on the detection performance of Air Traffic Control (Hereinafter referred to as ATC) primary surveillance radar (Hereinafter referred to as PSR), paper takes the scattering area of rainfall clutter as the object of study. By using the equivalent sphere as the equivalent model, the RCS mathematical statistical model of rainfall clutter at different levels is quantitatively analyzed in combination with the rainfall clutter sign. At the same time, from the angle of the rainfall clutter itself and the wet radome with different thickness of water film, the influence of the detection distance of ATC PSR under rainfall condition is analyzed and studied.
Binocular vision mechanical arm system based on salient region target recognition
Authors: Li Ronghua , Wang Ruiliang , Tian Xueying 
Published: June 2020

Abstract
Robotic arms have been widely used in the sorting of manufacturing lines. During the sorting process of the robot arm, the captured speed and accuracy are core problems. In order to improve that, this paper proposes a binocular vision sorting robot arm system based on the target identification of significant regions. First, establish the target artifact training set and the Faster R-CNN algorithm is imported. A binocular camera is used to take pictures of the workpiece in the tray, and then deep learning algorithm is used to extract the significant area where the object is located. Image processing is performed on this region. Then, the three-dimensional coordinates of the workpiece centroid are calculated and passed to the manipulator for grasping and sorting. Because the significance area is extracted, the amount of calculation by the image processing algorithm is greatly reduced, and the speed is significantly improved. Finally, the picking and sorting experiment was designed. The results show that the system has high accuracy, strong robustness and strong portability.
Research on Geomagnetic Reconstruction Based on LSTM Neural Network
Authors: Wei He , Jing Wang 
Published: June 2020

Abstract
Geomagnetic observation data has unique academic research significance, and it is also an important national basic strategic resource. Based on the correlation and temporal characteristics of surrounding stations in geomagnetic observation data, this paper proposes the use of LSTM neural network method to predict the missing data of the target station based on the data of the reference station, and it is compared with the traditional BP neural and CNN neural network method. The results show that the LSTM neural network is superior to the BP neural network and CNN neural network in geomagnetic data reconstruction, and the predicted geomagnetic data has higher accuracy.
Multi-robot task assignment algorithm based on improved self-organizing mapping network
Authors: Guo Zhenyu , Liu Dan 
Published: June 2020

Abstract
In order to solve the problem of slow convergence speed and easy task conflict, a multi-robot task assignment algorithm based on improved self-organizing mapping network is proposed in this paper. Considering the redundancy generated in the solution, the self-locking factor is introduced to optimize. The neuron is then reselected when the path intersects to avoid the risk of collision. The simulation results show that the proposed algorithm can effectively reduce the solution time and the distance of multi-robot navigation compared with the traditional algorithm.
Design of Remote Monitoring System for Limb Rehabilitation Training Based on Action Recognition
Authors: Hu Wentao , Zhang Jiashuo , Huang Bailiang , Zhan Weida , Yang Xue 
Published: June 2020

Abstract
Aimed at the high cost of domestic rehabilitation medical care, the limited number of doctors, the shortage of training venues, and the lack of follow-up tracking for patients who recovered better after rehabilitation training, and a remote monitoring system to understand the patient’s rehabilitation situation, a kind of motion recognition-based Remote monitoring system for physical rehabilitation training based on motion recognition was proposed. From the perspective of machine learning and intelligent classification, the system uses the wavelet transform principle and Support Vector Machine (SVM) algorithm to inject intelligence into the remote monitoring system for limb rehabilitation training, so that doctors can receive patients walking and running energy characteristic and their movement distance data in the rehabilitation center, and based on this data to determine the patient’s recovery and rehabilitation training plan, the doctor can make a diagnosis for dozens or even hundreds of patients even if they never leave home, which greatly improves the efficiency of treatment, saves the corresponding manpower and material resources for the country and society t, and benefits the people.
Mission planning of multi base UAV Based on improved artificial bee colony algorithm
Authors: Zhang Xiaomeng , Hu Yongjiang , Li Wenguang , Yu Mingxin , Wang Jianyin 
Published: June 2020

Abstract
Aiming at the constraint that the take-off and landing position of Unmanned Aerial Vehicle (UAV) varies from time in maritime operations, a cooperative attack model of multi-base UAV is established. An improved artificial bee colony algorithm based on coding operation pool is proposed, which retains the basic idea of standard artificial bee colony algorithm, It draws lessons from RNA coding operation and introduces coding operation pool to ensure that the algorithm has better global search ability. The simulation results show that the cooperative attack model of multi-base and multi-UAV complies with the characteristics of modern maritime unmanned combat missions, and the proposed algorithm has strong optimization efficiency and optimization ability.
Data analysis and strategy evaluation in national defence science and technology tracking
Authors: Li Kan 
Published: June 2020

Abstract
Science and technology tracking can provide powerful decision-making information support for science and technology management, and it is an important reference unit for national defence science and technology development planning and technical route. In the context of confidentiality, patent and industry competition, there are transmission barriers between organizations for systematic scientific and technological information, and a large amount of disorderly and excessive point information will lead to information disaster, lack of effective data and tracking methods for quantitative analysis, and the demand side is at a loss. Based on the combination of literature retrieval and data analysis, this paper selects the association between texts through co citation analysis, explains the relationship between tracking targets through co word analysis, and finally selects groups with similar or common characteristics as tracking targets. This paper explores a new way of science and technology tracking, which can provide reference for national defence science and technology tracking.
Empirical analysis of I-GBDT to improve the accuracy of mass appraisal method
Authors: Wang Jiaqi , Sui Jingqi , Zhang Ziyan , Qi Jiabao , Liu Nan , Lv Junna 
Published: June 2020

Abstract
The transactions related to real estate in social and economic activities need to appraise the market value of commercial housing. In order to improve the accuracy and efficiency of mass appraisal model (MA), the paper proposed the Improved-GBDT algorithm based on hybrid feature selection, which combines the concept of supervised learning and unsupervised learning, selecting appropriate features based on the impact of features on results and the changing trend of feature data. The feature weights can be calculated by Random Forest and XGBoost. Considering grey correlation analysis, the correlation degree among features can be calculated, and appropriate features can be selected. In the paper, we took the housing sales data from May 2014 to May 2015 in USA into simulation analysis. The simulation results show that IGBDT model has better accuracy and stability than SVR, RF and XGBoost. Compared with RF and XGBoost model under optimal parameters, IGBDT not only reduced the average absolute error of prediction by 98% and 11%, but also make the maximum relative error drop by 97% and 12%. The results show that IGBDT can provide a powerful reference in evaluating the property market value.
A Method of Searching for Optimal Coalition Structure for Solving Resource Scheduling Problem of Overall Load Balancing in Edge Computing Environments
Authors: Yanan Hu , Chunsheng Li , Kejia Zhang 
Published: June 2020

Abstract
The cooperative computing resource scheduling problem in the distributed computing environment is usually transformed into the problem about how to find the optimal coalition structure. However, affected by factors such as high node density and large number of concurrent tasks in edge computing environments, it is difficult to formulate resource scheduling schemes for overall load balancing in a large strategic space by traditional methods. In order to solve this problem, we proposed a method of searching for optimal coalition structure based on discrete particle swarm optimization (DPSO). Firstly, we converted the resource scheduling problem of edge computing into an optimization problem model, constructed a new structure of coalition structure to express the resource scheduling scheme, and gave key elements such as optimization objectives, decision variables, and constraint conditions. Secondly, we improved DPSO and designed M-ary discrete particle swarm optimization (MDPSO): improved particle state determination method for adapting to the new expression of the coalition structure, and proposed a power series-based inertia weight computing method to improve the optimization speed and the optimal solution quality. Finally, we simulated real application scenarios and designed comparative experiments on the optimization speed, the optimal solution quality, stability, etc., which verifies that the method proposed in this paper has obvious advantages in solving the resource scheduling problem of overall load balancing in edge computing.
Topological Data Analysis of Two Cases: Text Classification and Business Customer Relationship Management
Authors: Yue Bi 
Published: June 2020

Abstract
Topological Data Analysis (TDA) is a rising and burgeoning field of data science, which provides a set of new topological and geometric tools to extract relevant features out of complex high-dimensional data. In this paper, we mainly study two papers: Topological Data Analysis of Time Series Data for B2B Customer Relationship Management [1]; An Introduction to a New Text Classification and Visualization for Natural Language Processing Using Topological Data Analysis [2]. We briefly introduce the topological concepts involved in the two cases, then compare and analyze the corresponding topological solutions. Accordingly, we summarize the advantages of TDA and finally draw a conclusion with some improvements to optimize them.
Review of research on partial discharge detection techniques of high voltage switchgear
Authors: Chao Zheng , Yongkang Zheng , Jun Zhong , Yongfu Wang , Zhang Haiyang Zhao , Bo Ren 
Published: June 2020

Abstract
Partial discharge inside a high-voltage switchgear can cause serious accidents. Partial discharge detection can reduce the incidence of accidents. This article summarizes the current research status of common high-voltage switchgear partial discharge detection technologies (Transient Earth Voltage or TEV, Utrasonic or AE and Ultra-High Frequency or UHF), introduces the research status of partial discharge model identification and localization of PD source, and finally looks forward to future research directions.
Multi-objective Ensemble of Regression Chains Prediction Algorithm for Pose Correction Errors of Precise Vision-based Printing Equipment
Authors: Wu Qiang , Zhan Zhenhui , Yang Shilin , Zhang Xianmin , Yang Lixin 
Published: June 2020

Abstract
To accurately know the working condition of the Precise Vision-based Printing Equipment (PVPE) and improve its printing accuracy and stability, this paper proposes a multi-objective ensemble of regression chains algorithm to predict the pose correction (position and orientation) errors of PVPE’s alignment platform. Since this algorithm uses XGBoost as a base learner, it’s called XGBoost Ensemble of Regressor Chain (XGB-ERC) prediction algorithm. The algorithm is verified by a test set, which is constructed based on experimental data of PVPE. The experimental result shows that the Mean Absolute Percent Error (MAPE) of this algorithm for pose correction errors x, y and θ is 6.868%, 6.495% and 5.342%, which is 25.1%, 27.6% and 16.8% higher than that of the traditional XGBoost single-objective prediction algorithm. The predicted result of the proposed algorithm can be used to compensate the correction errors of the alignment platform, which will help improve the printing accuracy and stability of PVPE, and promote the development of the field of Surface Mount Technology (SMT).
Analysis of the Application Countermeasure of Virtual Reality Technology in the Vision Simulation System
Authors: Yonghui Wu 
Published: June 2020

Abstract
Virtual technology is a computer simulation system based on new technology and visual technology to realize the creation and experience of virtual world. It belongs to the use of information technology computer system function for environmental simulation, and it uses the combination of multi-information fusion and 3D dynamic visual interaction and physical behavioral experience to build virtual and real scenes to make the entire environment reality. It lets users feel the real environment and interact with them. At present, the application of visual simulation system for virtual reality technology has been gradually popularized. This article first introduces the development of virtual reality technology, and briefly describes the specific characteristics of virtual display technology, and finally puts forward some countermeasures for the application of virtual display technology in the visual simulation system.
Nowhere to hide: Anonymous User Recognition Based on Multidimensional Trajectory Set
Authors: Ma Manfu , Lei Yueshen , Li Yong , Wang Changqin 
Published: June 2020

Abstract
Anonymous user identification is one of the core issues in the field of information security and e-commerce applications. Accurately identifying the implicit characteristics of users’ online behavior and the unique identity is important, it has theoretical significance and commercial application value. Based on Massive online clickstream data, this paper proposed AUI algorithms and multidimensional trajectory set model (MTS) to verify the feasibility anonymous user identification. Through the AUI algorithm and vectorization method, a unique multidimensional trajectory set is created for each user. Experiments show that compared with the decision tree and user document methods, the multidimensional trajectory set based on the click data stream has improved the accuracy of recognition and provides an effective solution for user recognition.
Research and Design of Online Learning System Based on Cloud Platform
Authors: Daochun Xia 
Published: June 2020

Abstract
The traditional online learning platform has limited educational resources and isolated resources, so it is difficult to support the operation of massive data. This paper designs an online learning system based on cloud computing platform to effectively integrate resources, reduce operation and maintenance costs. The platform uses big data and distributed computing technology to collect and process user access history data and log data, and uses collaborative filtering algorithm to recommend courses, so as to improve the efficiency of learners.
Research on Visual Perception Technology of Autonomous Driving Based on Improved Convolutional Neural Network
Authors: Wang Yucheng , Li Zhihao , Hao Hongye , Yang Hengyu , Zheng Yixin 
Published: June 2020

Abstract
With the continuous development of the world’s scientific and technological level and the continuous progress of society and economy, driverless cars have become the key research targets of the existing automotive industry. Based on the convolutional neural network theory, this paper redesigned the Faster R-CNN network framework to improve the target recognition accuracy and apply it to the unmanned visual perception system. At the same time, the improved algorithm is used to test the cars under different simulated travel conditions. The computer vision system toolbox of MATLAB was used to verify the effectiveness of the algorithm, and the driving scene designer was used to construct multi-sensor fusion and simulated road scenes, and the corresponding visual control system was designed to effectively realize the automatic code generation. The experimental results show that the average detection accuracy of the improved algorithm is higher, the algorithm is more feasible and robust, and it is helpful to promote the development of driverless cars.
The Properties and Application of Poisson Distribution
Authors: Zhao Jing , Zhang Fan , Zhao Chao , Wu Gang , Wang Haitao , Cao Xinyu 
Published: June 2020

Abstract
Poisson distribution is not only a very important distribution in probability theory, but also a useful tool to study the random events. Firstly, this paper gives a simple introduction of Poisson distribution and some characteristics of the Poisson distribution. Secondly, some properties of Poisson distribution are introduced and proved. In the end, some application of Poisson distribution are introduced, such as the application in the rare event and in daily life. We also do some analysis about the application of Poisson distribution.
Research on Terminal Management Technology Based on Distribution Management System
Authors: Yinghua Song , Gang Wang , Zhongwei Li 
Published: June 2020

Abstract
With the continuous development of distribution automation technology and the rapid growth of the number of distribution equipment, this paper analyzes how to carry out the operation management of distribution network equipment and the key technology of application based on the existing distribution management system. The key technologies include the unified management, automatic control and online monitoring of equipment. Through the research of related technologies, the rapid and effective management and control of mass distribution equipment is realized, the management efficiency of equipment is improved, and the technical support means for the rapid construction of distribution network are provided.
Probabilistic Load Forecasting of Adaptive Multiple Polynomial Regression considering Temperature Scenario and Dummy variables
Authors: Li Jiang , Ren Liyang , Wang Baocai , Li Guoqing 
Published: June 2020

Abstract
The monthly or yearly low accurate history data always leads to the low prediction-accuracy for load forecasting. We use temperature data from Sydney, Australia and the New South Wales Natural Load Dataset. To improve the data-based forecasting accuracy and time related scenario, this paper builds an adaptive multiple polynomial regression model considering temperature scenario and dummy variables. These dummy variables are divided into three aspects: trend variables, date variables and temperature variables. Trend variables are used to predict the whole economic development and user habit. Date variables are introduced to deal with the characteristics of working days and holidays. Cubic function for temperature variables from Australia and the New South Wales electric load history data is constructed to describe the relationship between load and temperature scenario. A temperature scenario is generated by considering the different loads of different seasons and the probability search of different scenarios. The load forecasting interval under different scenarios is given and analyzed by using dummy variables. At last, the method is validated based on the history data in a certain area. The prediction result with high accuracy shows clear intuitive and powerful interpreting ability, which can provide reliable decision basis for long term load forecasting. After simulation analysis, the accuracy of load forecasting based on 3-year history increases by 3.8%.
Research on Robust Design and Optimization of Embedded Network Electronic Information System
Authors: Yang Pan , Zhou Xinliang , Zhou Xinliang , Jia Jianhao 
Published: June 2020

Abstract
At present, in the process of designing and optimizing the robustness of embedded network electronic information systems, it is found that many factors in the system have some correlation with robustness, and there are individual differences in the factors affecting robustness, which also have different effects on robustness at the same time. The optimization design of the calculation method for the robustness of the embedded network electronic information system is organically combined with chaos and particle swarm optimization, through experiments and simulations, it proves that the optimized design has high accuracy.