bluuebunny/crossref_metadata_2025_split huggingface dataset:
Massive AIS data storage and query based on Hadoop platform
Authors: Lv Taizhi , He Chenyong , Zhang Juan , Song Zhiyang 
Published: June 2021

Abstract
There are nearly 50 million AIS data generated every day in the Yangtze River Basin. Efficient storage and query of AIS data become the key problem of smart maritime administration. To overcome this problem, a distributed storage and query scheme based on Hadoop platform is proposed. Firstly, the original AIS message is parsed, the abnormal data is removed and the parsed data is pushed to the Kafka message queue. Secondly, the messages in the queue are pushed to different servers to complete the storage of real-time cache, historical trajectory and regional information. Finally, the query interface is provided to realize the real-time query of ship status, trajectory and regional ship in a certain period of time. The real applications show that compared with the traditional relational database, the solution can meet the requirements of real-time query and has high scalability.
A Deep Learning Method for Target Pose Estimation of Planetary Surface
Authors: Lu Huang , Xiaoyan Mao , Yan Xing 
Published: June 2021

Abstract
The key technical challenge of 6D target attitude estimation from RGB-D image is to make full use of 2d and 3D image point cloud data. Previous traditional target pose estimation methods either extract information from RGB images and depth, respectively, or use expensive post-processing steps that limit their performance in highly cluttered scenarios and real-time applications. Therefore, in this paper, a method is proposed to estimate the 6D attitude of a group of known objects from RGB-D images. The network used is a heterogeneous architecture, which processes two data from different dimensions separately, and uses a new dense fusion network to extract pixel-level dense feature embedding, and estimate the target position and attitude from it. In addition, an end-to-end iterative attitude optimization method is integrated to further improve attitude estimation, while real-time visualization of point clouds and rendering of bounding boxes are realized. Experimental results show that this method is effective on open source data sets.
Chinese grammatical error diagnosis based on sequence tagging methods
Authors: Wang H , Zhang Y J , Sun X M 
Published: June 2021

Abstract
In view of the problems such as exploding gradient or vanishing gradient or inefficiency caused by parallel problems when traditional neural networks deal with long text grammar error correction. In this paper, Chinese grammatical error detection is proposed as a sequence tagging problem for named entity recognition, and a Chinese grammatical error detection model based on BERT BILSTM CRF is designed and implemented by using Bert model to construct word vectors. Meanwhile, three groups of comparative tests were designed: CRF, BILSTM CRF and BERT CRF. In the end, BERT BILSTM CRF model achieves the error detection accuracy of 66.67% and 52.94% in M (missing error) and W (ordering error), respectively. BERT CRF and CRF models have also achieved good results in S (selection error) and R (redundant words). The research shows that it is feasible to solve grammatical error detection as a sequence labeling problem. On the other hand, a Chinese grammatical error detection model based on BERT BILSTM CRF is proposed for the first time.
Analysis and Forecast of Urban Rail Transit Network Based on LTE Signaling
Authors: Shunyu Yao , Jinbai Zou , Jiaming Jiang , Zhukai Shen 
Published: June 2021

Abstract
According to the requirements of LTE-M in rail transit safety, this paper collected various data in LTE-M network without affecting the normal operation of the network, screened effective information from the mass data, and systematically designed the software with data analysis technology. Using the neural network algorithm to predict the signaling data, we can judge the future condition of the network, adjust the network in time and prevent the possible failure in the future.
Pairwise constraints cross entropy fuzzy clustering algorithm based on manifold learning and feature selection
Authors: Cai Wei , Xu Shengbing , Zhang LiangJun , Liu Jiongzhi , Chen Peixuan 
Published: June 2021

Abstract
In weakly supervised learning, it is difficult for us to utilize pairwise constraints information in feature selection. In order to solve the problem, we propose Pairwise constraints cross entropy fuzzy clustering algorithm based on manifold learning and feature selection (FCPC-LEFS). There are four phases in our approach: 1) Generate pseudo label; 2) Dimension reduction by Laplacian Eigenmaps; 3) Feature increment and selection; 4) Cross-Entropy semi-Supervised Clustering Based on Pairwise Constraints. We apply our approach to three UCI datasets and a COVID19-CT image dataset. Experiments show that our manifold learning and feature selection method are able to increase improve the clustering performance.
Video reconstruction of generative adversarial network based on global perception
Authors: Shende Chen , Xiaoman Wang , Wenbo Jing 
Published: June 2021

Abstract
Aiming at the problem that the current video reconstruction can not take into account the high perception quality and high temporal correlation, a video reconstruction of generative adversarial network based on global perception is proposed. The generator adopts a recurrent and iterative network architecture. In order to make up for the lack of extraction of global information by the frame alignment network, the alignment network combines the global information extracted by the global information perception to perform frame alignment. Through the static temporal loss and the temporal statistics loss, combined with the relative discriminator of sequence frames input, the temporal correlation of the generated images sequence is improved. Experimental comparisons were performed on the Vid4 and REDS test sets, and the best image perception quality index (LPIPS/NIQE) were obtained, as well as better temporal correlation (tLPIPS), reaching 0.192/3.417/0.328 and 0.138/3.223/0.217. Experimental results show that the proposed method can effectively improve the perceptual quality of reconstructed video frames and has good temporal correlation.
Improved RRT-Connect Algorithm for Urban low-altitude UAV Route Planning
Authors: Jin Haonan , Cui Wei , Fu Huaye 
Published: June 2021

Abstract
Path length and computation time in path planning seriously affect the safety and efficiency of urban low-altitude UAV flight. This paper improves RRT-connect Algorithm for path planning, the search step length, parent node selection and branch orientation selection are optimized to reduce the path length and algorithm time. Starting from the shortest parent node of the two trees, track search is carried out in the Middle Direction, the search step length is changed according to the field of view, the search step length is cut down quickly through the open field, and the search step length in narrow space is reduced, at the same time, the growth direction of the main branch of the search tree is controlled by the angle to reduce the probability of the UAV turning sharply. Simulation experiments show that the proposed algorithm has better time and space advantages and convergence than the current popular algorithm, the comparison results show that the optimized Algorithm RRT-connect is superior to the current flow Algorithms RTT-extend and Lazy-RTT in terms of the time consumption of short-time fast search and the length of path generation.
Detail Reconstruction of 3D Face Model from Single Image
Authors: Xu Hongxin , Lan Ruoming , Li Tianping 
Published: June 2021

Abstract
With the development of computer vision technology, 3D face model reconstruction technology has a great breakthrough. In view of the current popular deep learning method, we use an algorithm based on neural network to restore the details of 3D face model. We design a high frequency detail generation network to generate displacement map based on the texture extracted from the original image. The displacement map contains a large number of high frequency detail features of the target face. By embedding the displacement map into the face model, the details of the model can be restored.
Aggregating Local Features of Convolutional Neural Network for Material Image Retrieval
Authors: Qing Qing 
Published: June 2021

Abstract
Large-scale microscopic images in materials science need to be indexed and managed using practical management tools. Content-based Image Retrieval (CBIR), which indexes and searches images based on the image features, allows for long-term data management in large-scale image datasets. Considering the difference between material microscopy images and natural ones, we propose a novel CBIR method for material microscopic images. In the proposed method, convolutional neural networks (CNN) are used to extract local features from an image, and the scale-invariant feature transform (SIFT) model is used to generate a keypoint density map (KDM). Experiments on a material microscopic image dataset show that the proposed method achieves an approving retrieval performance.
A Multi-view Image Sets Classification Based on Graph Convolutional Neural Network
Authors: Liu Xun , Chen XiaoLin , Xia GuoQing , Chen HuaZhen , Ye HeZhong , Chen ZhanChi 
Published: June 2021

Abstract
Multi-view images represent the target object from various perspectives. We propose a new spectral graph convolutional neural network (BSGCN) for multi-view image sets, which uses spectral graph convolution technology to process multi-view image sets. With the batch normalization techniques, the BSGCN speeds up the model convergence and improves the generalization capabilities of the model. We analyze the performance of two variants of BSGCN and its model by experiments on multi-view datasets. Experimental results show that the BSGCN is 2.84% and 7.26% higher than the classic graph CNN [17] on Modelnet10 and Modelnet40 multi-view datasets, respectively, and 0.34% higher than the classical CNN [6] on Modelnet40 multi-view dataset.
Research on a method of digital map generation for tracked equipment maneuvering on soft ground
Authors: Mingming Liu , Meiling Zhu , Yanxi Ren , Jingjun Yuan , Jibin Hu 
Published: June 2021

Abstract
The mobility of equipment refers to the ability of fast driving under various road, ground and terrain conditions that may be encountered in the tactical background. In recent years, with the rapid development of national defense and aerospace, tracked equipment is required to reach more and more areas. However, due to its low bearing capacity and low adhesion capacity, soft roads such as sandy land, swamp, farmland, tidal flat and lunar soil are easy to cause subsidence, resulting in low driving efficiency or even inability to drive, which seriously restricts the feasibility of driving equipment. In this paper, considering the characteristics of tracked equipment, through the superposition of multi-layer spatial information such as slope, aspect, soil and elevation data, based on equipment dynamics, fuzzy theory and ant colony algorithm, the generation of mobile digital map and mobile path planning of tracked equipment under soft road and complex terrain are studied, which has important practical significance.
Application of Electronic Technology in Automobile Safety System
Authors: Li Mengyin , Ge Xinye 
Published: June 2021

Abstract
This article analyzes the factors that affect the stability of the vehicle safety system. The research content of this article includes own factors, environmental factors, human factors. The author studies the specific application of electronic technology in the active safety processing, passive safety processing, and post-accident processing of automobile safety systems. The author analyzes the development of intelligent electronic technology, diversified development, energy-saving development and other trends. The purpose of this article is to give full play to the application value of electronic technology and improve the reliability of the operation process of the automobile safety system.
The influence of time-varying mesh stiffness on natural characteristics of planetary gear system
Authors: Zhang Keming , Zhou Tao , Shao Yimin , Wang Guochao 
Published: June 2021

Abstract
Modal property is the crucial factor that can influence the vibration characteristics of the planetary gear system. Previous studies used time invariant mesh stiffness (TIMS) to study the natural characteristics. However, with the rotation of the gear, the meshing stiffness will change periodically which will lead to changes of modal properties. Based on these properties which are ignored in previous studies, a translational-torsional free vibration model base on time-varying mesh stiffness (TVMS) rather than time invariant mesh stiffness (TIMS) is established in this paper to study the modal properties of the planetary gear system, the effects of TVMS on the natural characteristics are investigated. The results show that the meshing phase difference and the change regulation of the time varying natural frequencies are correlated, and there is a dramatic variation of higher order natural frequencies at the contact teeth change points. Furthermore, a useful conclusion can be drawn that takes TVMS to calculate higher order frequencies has certain advantages over traditional methods.
Research and Analysis on firing accuracy of Naval Gun
Authors: Liu Ziyu , Chen Weiyi 
Published: June 2021

Abstract
This paper expounds the significance and function of studying the firing accuracy of naval gun weapon system, and briefly analyzes the firing error source, firing accuracy evaluation and firing error compensation method of naval gun. Through the establishment of the analysis model of firing accuracy, the principle of error sources affecting firing accuracy is clarified. Taking the projectile tracking method as an example, different firing correction methods of naval gun are analyzed, so as to improve the firing accuracy of naval gun.
The design of eccentricity ratio, relative clearance and length-to-diameter ratio of water-lubricated journal bearings under different working conditions
Authors: Liu Ying , Gao Gengyuan , Yin Zhongwei , Jiang Dan , Ding Ning 
Published: June 2021

Abstract
This paper investigated the eccentricity ratio, relative clearance, and length to diameter ratio under different load carrying capacities and journal rotation speeds, aiming to provide design reference of eccentricity ratio, relative clearance, and length to diameter ratio in water-lubricated bearing design process. The computer fluid dynamics was adopted to analyze eccentricity ratio, relative clearance, and length to diameter ratio of water-lubricated bearing. The cavitation effect of water was considered in the analysis. The simulation results of eccentricity ratio were verified by experiments. In different journal rotation speeds, the relative clearance had little influence on the maximum water film pressure and load carrying capacity. The numerical number of maximum water film pressure and load carrying capacity was in a small range. Designing chart of eccentricity ratio, relative clearance, and length to diameter ratio was provided.
The Vehicle behavior under Braitenberg Vehicle 4 Driven by Gaussian-like function
Authors: Guo Shuyi , Xu Henghao , Wang Yuxuan 
Published: June 2021

Abstract
The goal of this paper is to recreate the Braitenberg vehicle 4 in Simulink via MATLAB. The attempt of using the Gaussian-like function to control the vehicles’ trajectory would result in the further analysis of the so-called behavior, which would then be classified into two types related to the term in the field of psychology “invasion” and “retraction”. The tool we used is the Robotics Expansion Pack in Simulink via MATLAB. The results were positive and served as excellent support of our assumption. By controlling the shape of the gaussian-like function, the vehicle would possess the behavior pattern of aggressive invasion or retraction in fear. The result sets a foundation for further research in bio-inspired robotics, it also provides a new attempt in controlling the Braitenberg vehicles.
Research on numerical control machine fault diagnosis based on distribution adaptive one-dimensional convolutional neural network
Authors: Yuying Zhang , Yi Lu , Jing Zhao 
Published: June 2021

Abstract
Numerical control machine is a high-precision and high-automation equipment, if the problem occurred in operation, it can affect the processing conditions of the machinery parts first. If the fault is aggravated, it can eventually cause the numerical control machine to stop. Spindle bearings and tools are the most vulnerable parts of numerical control machine. Previously, resonance demodulation technique was used for bearing fault diagnosis. Empirical analysis or neural network was used for tool fault diagnosis. However, the numerical control machine is an entirety, the fault is usually caused by multi-dimensional factors, the above method doesn’t work when two types of faults occur at the same time. To diagnose faults of numerical control machine, a fault diagnosis model named distribution adaptive deep convolutional neural networks (DADCNN) was proposed. This model was based on One-dimensional convolution algorithm. The Batch Normalization algorithm was involved to overcome the problem of changing data distribution in the middle layer. The t-SNE algorithm was used to visualize and view the feature classification results. Experiments show that the accuracy of this model can reach 90.29%, and it has good fault diagnosis capabilities.
Research on the Microstructure and Mechanical Properties of WC-TiC-Co/steel Joints
Authors: Zhang Feifei , Zhang Henglei , Yang Zhongyuan , Du Fengzhen 
Published: June 2021

Abstract
Wear-resistant layer of cemented carbide on steel can improve its wear properties in particular areas, such as oil drilling. Herein, the WC-TiC-Co/steel joint, the WC-TiC-Co wear-resistant layer covers the steel surface, is fabricated by infiltration brazed, using Cu-Zn filler alloy and WC particles as solder. The microstructure and mechanical properties of as-obtained joint are investigated. The Longitudinal interface has good bonding and the existence of transverse gaps are surveyed on interface between WC-TiC-Co and solder. The element Mapping test shows the distribution of Co element in WC-TiC-Co block is uneven. And the present of element interdiffusion forms the diffusion layer in the joining zone between base materials and solder. The hardness and phase tests indicate that Co3Fe7, W2C phase were produced during brazing, but there are few impacted on the hardness by Co3Fe7, W2C phase.
The Mechanical Property and Strengthening Mechanism of the Mg-0.5Zn-0.2Mn-0.2Sm-0.3La alloy
Authors: Fu L , Guo C R , Lu L , Jia Z , Le Q C 
Published: June 2021

Abstract
Tensile properties at TR with different stretch velocity, and tensile properties at T150°C were investigated in this study, and the strengthening mechanism was discussed preliminarily. Firstly, tensile properties of the Mg-0.5Zn-0.2Mn-0.2Sm-0.3La alloy at TR with the stretch velocity increases from 1 to 6 mm·min−1, and results suggest that the studied alloy exhibits low strength sensitivity as both of Rm and Rp0.2 maintaining at 240 MPa and 190 MPa, which is attributed to the not enough time for the dynamic recrystallization at the higher deformation velocity. Meanwhile, as the stretch velocity increases, although A increases first and then decreases because of the more stress concentration near grain boundaries, A maintains above 20%, suggesting the good plastic sensitivity. Secondly, when stretched at T150°C both of Rm and Rp0.2 of the Mg-0.5Zn-0.2Mn-0.2Sm-0.3La alloy decrease compared to that at TR, which is attributed to the increased dislocation movement at the higher temperature, while A increases at T150°C than at TR because of the decreased critical shear stress at the higher temperature. Even though, because the Mg12La phases impede the dislocation movement and the Sm atoms result in the solution strengthening, the mechanical properties of the Mg-0.5Zn-0.2Mn-0.2Sm-0.3La alloy are still higher than that of the Mg-0.5Zn-0.2Mn alloy at the higher temperature.
Analysis of mechanical properties and performances of laser melting forming of H13 mold steel in selected areas
Authors: jing Xiaowei , Chu zhong 
Published: June 2021

Abstract
When preparing H13 molds by laser melting in selected areas, due to the complex force direction of the mold during use, it is necessary to analyze the mechanical properties of the specimens at different forming angles. By preparing specimens with different forming angles (0°, 30°, 45°, 60°, 90°), tensile specimens of different forming angle specimens were made and the results were analyzed. The results show that there are many defects between layers, resulting in anisotropy of the mechanical properties of the sample. The 0° sample has the highest mechanical performance, with a tensile strength of 1933.1 MPa and an elongation of 11.1%. As the forming angle increases, the resistance Tensile strength and elongation showed a downward trend. The 90° sample had the lowest tensile strength, with a tensile strength of 1552.8 MPa and an elongation of 6.42%.
Structural parameter effect of a railway hydraulic damper on its dynamic damping characteristics
Authors: Wang Wenlin , Li Rongyong , Zhu Shan , Wu Yongming 
Published: June 2021

Abstract
A parametric dynamic damping characteristics model, which includes the dynamic force equilibrium equation, the flow continuity equation, the flow losses model, the comprehensive stiffness model and the valve dynamics, is established and validated by experiments. Numerical simulation of structural parameter effect on dynamic damping characteristics of the hydraulic damper is performed. The analyses show that increase of inner tube height would cause drop of dynamic stiffness of the damper, however, increase of piston diameter would lead to obvious increase of dynamic stiffness and dynamic damping coefficient, therefore, the biggest piston diameter under constraint is usually used in engineering; in addition, fluid leakage would apparently decrease damping capability of the damper, increase of leakage coefficient would lead to significant drop of dynamic stiffness and dynamic damping coefficient, and also remarkable damping force lag. The obtained full parametric model and analysis result in this work would be instructive and meaningful in further railway vehicle dynamics research and design procedures.
Application of Photocatalytic Nanomaterials in Photoelectrochemical Biosensors
Authors: Hu Yiliu , Xu Zhuoyuan 
Published: June 2021

Abstract
Recently, photoelectrochemical (PEC) biosensors are becoming the rising star in biosensing analysis because of their high potential to construct different biosensors with high sensitivity, selectivity, and low cost. It is well-known that semiconductor materials with intrinsic large band gaps limit their applications in the range of ultraviolet (UV) irradiation. Moreover, UV light is somewhat too energetic and has a destructive effect on biomolecules. By introducing metal nanoparticles, carbon-based nanomaterials, and even organic molecules into semiconductor materials, the light response window for these functionalized nanocomposites can be extended to the visible light region. In this review, we mainly discuss PEC biosensors’ applications based on different state-of-the-art nanocomposites in three aspects, including the environmental field, food safety field, and medical field. The design principle and performance of PEC biosensors are systematically analysed. And we also briefly look forward to the development trend of this kind of PEC biosensors in the future.