bluuebunny/crossref_metadata_2025_split huggingface dataset:
Weighted Laplacian Method and Its Theoretical Applications
Authors: Xu Shijie , Fang Jiayan , Li Xiangyang 
Published: March 2020

AbstractRecently, a class of multilevel graph clustering (graph partitioning) algorithms have been extensively studied due to their practical utility. Although these newly proposed algorithms work well, there is still not many theoretical guarantees for the quality of partition in these existing methods due to their intrinsic heuristic properties to a great extent. In this paper, we propose a novel weighted Laplacian method for the multilevel graph clustering problem with more powerful theoretical background to further improve clustering performance mainly in terms of accuracy. Since our algorithm inherits the virtues of spectral methods, it possesses a friendly optimization property since it can produce the global optimal solution of a relaxation to the weighted cut on the coarsened graph in the middle stage. Meanwhile, the multilevel strategy can make it possible for our method to produce final clustering results with reasonable time range. Additionally, the weighted graph Laplacian is also suitable for doubly-weighted graph, which will endow our algorithm with a potential wide range of applications. The experimental results verify that our weighted Laplacian methods is indeed superior over existing algorithms in terms of clustering accuracy while also maintains comparable clustering speed.
On the Operability of Computer-aided Translation Software
Authors: Peng Hao 
Published: March 2020

Abstract
Under the context of globalization, communications between countries and cultures are becoming increasingly frequent, which make it imperative to use some techniques to help translation. This paper is to explore the requirements of computer-aided translation software during the translation practice, optimize the design of computer-aided translation software, and carry out a research on its operability in translation.
Multi-task convolution neural network regression prediction model based on vis-NIR spectroscopy
Authors: Jia Dongning , Du Zehua , Huang Xianqing , Li Kexin , Ren Guoxing , Li Ruixue 
Published: March 2020

Abstract
Aiming at the limitation of the current time-consuming and low-efficiency of regression prediction model based on visible and near infrared (vis-NIR) spectroscopy, this paper uses multi-task convolutional neural network model to predict various attributes such as soil total carbon, total nitrogen and alkali nitrogen. Considering the influence of the soil itself and the uncertain factors in the measurement process on the prediction results, the near-infrared soil spectral data is subjected to multi-scatter correction pre-processing, and the model is input and trained to optimize various parameters in the network. The experimental results verify that compared with the single attribute model, multi-task learning optimizes all output variables, maintains the correlation between data, improves learning efficiency and reduces training parameters and training time. The network model also achieves excellent evaluation criteria.
Multi person pose estimation based on improved openpose model
Authors: Xing Jiayuan , Zhang Jun , Xue Chenxing 
Published: March 2020

Abstract
In order to solve the problem of multi person pose estimation, a human body pose estimation model framework based on openpose from American Carnegie Mellon University is presented, which uses the COCO (Microsoft common objects in context) data set to augment the data, optimize the network structure and retrain, adjust the learning rate properly and the convergence speed in the early stage of the model, finally using the model to carry out research on human key point detection and visualizing analysis in a multi-person complex scene. It is found that the improved openpose model adopts a bottom to up detection strategy to avoid the influence of the number of human bodies on real-time performance, and has higher key point detection efficiency than the top to down method. The study provides a real-time position feedback scheme for human pose estimation in complex scenes.
Research of shopping recommendation system based on improved wide-depth network
Authors: Wang Shanshan 
Published: March 2020

Abstract
With the development of e-commerce, there are more and more commodities. How to recommend the commodities that users are interested in quickly and accurately has become an important research topic in the field of e-commerce.we propose a product recommendation algorithm based on DeepFM network. Firstly, we embed the user’s purchased products, and transform the sparse feature into the low-dimensional dense feature, while the user’s personal attribute features can express the user’s purchase intention to a certain extent, and also use embedded coding to transform the features.DeepFM considers both wide and deep (i.e. low-level and high-level) at the same time to further improve the generalization ability of the model. So we use DeepFM to predict the interest of users in purchasing goods.Learning the expression of user’s interest from user’s purchase and personal preference, so as to accurately predict user’s purchase behavior.Finally, we use the real record data set purchased by online users to evaluate the effect of the model, and compare it with other models to verify the effectiveness of the model.
Change Detection Based on IR-MAD Model for GF-5 Remote Sensing Imagery
Authors: Xu Guibin , Li Huafeng , Zang Yuwei , Xie Lianke , Bai Chunxiao 
Published: March 2020

Abstract
GF-5 is the domestic full spectrum satellite with the most spectral bands, and it can comprehensive observe earth and atmosphere, the data can effectively monitor the changes of ground objects. However, due to the high wave dimension and large data of the hyperspectral remote sensing image, which reduces the processing and operation speed, and brings great uncertainty to the accuracy. In order to improve the accuracy and processing speed of hyperspectral imagery change detection, a method of iterative weighted multivariate change detection based on IR-MAD is proposed. In this paper, the high-resolution remote sensing image of GF-5 is used as the data source. After geometric correction, removal of bad line and other pre-processing methods, the change detection results are obtained by the iterative weighted multivariate (IR-MAD) change detection method. The experiments show that: the algorithm in this paper is compared with change vector analysis (CVA) change detection, principal component change vector analysis (PCA-CVA) change detection method, and iterative weighted multivariate (IR-MAD) detection method without principal component extraction. The detection accuracy of this method is high, and the error rate and missed rate are also low.
Hierarchical neural network for facial attribute transfer
Authors: Guo You , Shang Yanlei , Zhang Xiaofang 
Published: March 2020

Abstract
The facial attribute transfer refers to generating a face image with desired attributes while preserving other details. In the existing methods, some of the them consider the independence among attributes but neglect the integrity. It may result in information loss and lead to distorted generation. The others ensure the integrity at the cost of insufficient independence limits. The generated images will be partially blurred. In this paper, GAN and Variational Autoencoders (VAE) structure are incorporated to preserve the details while attribute transfer occurs. The concept of hierarchical latent representation is introduced to realize the attribute independence. These methods work with each other forming an effective network for facial attribute transfer, referred as Hi-GAN. Experiments on the CelebA dataset show that our model can output clearer and more realistic images on facial attribute transfer.
ScaleLK: Registration of Point Clouds with Different Scales Using Deep Learning Methods
Authors: Xu Shuai , Shang Yanlei 
Published: March 2020

Abstract
3D point clouds are widely used in numerous research and applications, such as autonomous driving, industrial robots, and augmented reality, to represent the spatial structure of objects. The 3D point cloud registration aims to transform the source point cloud into the same coordinate system with the template point cloud, which is of great significance for the 3D reconstruction of the real world objects. ICP [1] is one of the most classic point cloud registration algorithms but it still has problems with efficiency and initialization. With the help of deep learning, PointNetLK [7] becomes a state-of-the-art point cloud registration method. Although PointNetLK is efficient and robust to some extent, it is not able to register point clouds with different scales. In this paper, we propose ScaleLK, an approach for registration of point clouds with different scales using deep learning methods. We have trained a feature extractor which supports scale feature and used this feature for registration of point clouds with different scales. We describe the architecture and compare its performance with other methods.
Text Classification for Marine Natural Products Literature
Authors: Dai Xuebing , Liu Wei , Gao Chunxiao , Wei Zhiqiang 
Published: March 2020

Abstract
We propose a text classification model MNPC-BERT for marine natural product literature based on BERT pre-training model. The marking task of Masked LM performed at the input stage of the pre-training phase is moved to the encoder of the transformer, which solves the problem of inconsistency between the pre-training phase and the fine-tuning phase. We have constructed a data set from a number of literature data sources containing different types of marine natural products literature, as well as a variety of different types of non-marine natural products literature. Compared with the classical algorithm, the algorithm proposed in this paper can better complete the classification based on existing data set.
Diabetes Prediction Based on XGBoost Algorithm
Authors: Li Mingqi , Fu Xiaoyang , Li Dongdong 
Published: March 2020

Abstract
Exploring important features of diabetes through analytical methods of data mining is able to predict and prevent diabetes. This paper proposes a diabetes prediction algorithm based on XGBoost algorithm with the numerical features being separated while some important features are extracted from the text features of experiment data. Experiment results show that accuracy of diabetes prediction based the improved XGBoost algorithm with features combination is 80.2%, which is feasible and effective method for diabetes prediction.
Research on Fault Diagnosis Method of Gearbox Based on FPGA and LabVIEW
Authors: Chen Zhuo , Yang Xiufang , Cheng Xiaolong 
Published: March 2020

Abstract
Gearbox is an important part of mechanical equipment. Its running state directly affects the working performance of the whole machine. Therefore, it is of great practical value in the research on gearbox fault diagnosis method. A gearbox fault diagnosis system based on FPGA and LabVIEW is designed in this paper, which consists of upper computer and lower computer. The lower computer uses FPGA as the core device to sample, convert, store and transmit signals, while the upper computer takes LabVIEW as the development environment to complete the signal processing and interface design of the fault diagnosis system. The experimental results show that the system is convenient for operating and user interface friendly. It can realize the real-time acquisition and transmission of the vibration signal of the gearbox, and the display and analysis of the vibration signal of the gearbox.
ESR temperature compensation algorithm based on BP neural network
Authors: Liu Li , Chen Xiaohui , Wu Yuqing 
Published: March 2020

Abstract
Erythrocyte sedimentation rate (ESR) is an important medical test parameter, changes in ESR value can reflect changes in the condition,ESR measurements are susceptible to ambient temperature.This paper proposes a temperature compensation algorithm for ESR measurement based on BP neural network,temperature compensation can be ESR value detection result,to further improve the measurement accuracy erythrocyte sedimentation rate.Firstly, a neural network model of ESR temperature compensation is established. Secondly, learning training samples of the network are obtained and the sample data is pre-processed and network trained. Finally, the network prediction effect is tested to generate an optimal ESR temperature compensation network model.The algorithm proposed in this article can use the neural network method to correct the erythrocyte sedimentation measurement value, and can quickly and accurately achieve temperature compensation of the erythrocyte sedimentation value. This algorithm not only makes the accuracy of the compensation link within the measurement tolerance range, but also for the temperature without training Points have predictive compensation effects.
Simulation and analysis of pedestrians in the station of urban rail transit
Authors: Liu Xia , Li Yazhuo 
Published: March 2020

Abstract
The hall of Wuhan Dazhi road station of urban rail transit is modelled by Anylogic. Based on the data surveyed in the morning peak period of some workday, walking paths of pedestrians are simulated and utilization of equipments is analyzed. To reduce pedestrian waiting time, security checkpoints are added. Then the average passage time going through the station hall is reduced.
Automatic feed formulation method based on Differential Evolution algorithm for precision feeding of dairy cows
Authors: Guo Peng , Zhang Longhao 
Published: March 2020

Abstract
To improve the efficiency of feed utilization and reduce the feeding cost, we proposed a feed formulation method based on Differential Evolution algorithm. Individuals were constructed according to the content of each raw material in the feed formulation and the lowest feed formulation cost was taken as the objective. The feed formulation was solved by Differential Evolution algorithm. Using NRC (2001) prediction model of main nutrient requirement of dairy cows and the data of common feed of dairy cows in China Feed Database, we carried out the experiment of feed formulation for lactating dairy cows. Compared the feed formulation result of our method with those of Linear Programming method, our method performed better.
Research on the realization and optimization of FFTs in ARMv8 platform
Authors: Du Qi , Huang Hui 
Published: March 2020

Abstract
Fast Fourier transform (FFT) is a basic algorithm in digital signal processing and has been widely used in engineering, science and mathematical calculation. With the wide application of ARMv8 processor, it is more and more important to study FFT algorithm to realize high performance on ARMv8 platform. In the current open source FFT library, FFTW packages are mainly suitable for ARMv8 platform, while FFTW and FFTs packages are mainly suitable for ARMv7 platform. This paper implements and optimizes FFTs software package on ARMv8 platform, making it suitable for ARMv8 processor. The experimental results show that the performance of FFTs is improved by about 18% compared with FFTW in ARMv8 platform.
The Modeling Method of Digital Twin Models for Machining Parts
Authors: Zou Qi , Hou Zhixia , Wang Mingyang , Jiang Shan 
Published: March 2020

Abstract
This paper introduces digital twin models for machining parts based on key features and gives the modeling method. In this paper we propose three layers structure of the digital twin model, including geometric layer, data layer and document layer, in order to make machining process optimization and quality prediction. The digital twin model realizes the expression of physical parts in digital space and the information correlation with theoretical processing features. Besides, this paper also analyzes the potential application scenarios and development trends of digital twin models of machined parts.
Recently Progress on Polylactide/Nanocellulose Nanocomposites
Authors: Xu Zhuoyuan 
Published: March 2020

Abstract
The development of renewable resources is a trend in the future. Polylactide (PLA), as one member of bio-polymers, has obtained a widely focus in industry and academy because of its renewability, biodegradability, biocompatibility and good thermomechanical properties. PLA is used in a wide variety of applications such as biomedical, food packaging, electrical and electronic equipment, and the automotive industry. However, PLA still has some shortcomings, such as lower heat distortion temperature, slower crystallization rate, lower thermal resistance, etc. These shortcomings also limit the further widespread use of PLA. One effective method to expand and improve these properties of PLA is adding nanofillers. This review summarizes and describes the role of nanocellulose as a filler in the modification of PLA matrix and some of the problems faced.
Machining Quality Evaluation and Optimization of Aircraft Structural Parts Based on Multi-factor Analysis of Variance
Authors: Xie Bukun , Wang Wenli 
Published: March 2020

Abstract
Quantitative evaluation of machining quality of aircraft structural parts plays an important role in improving machining quality of parts. The rate of error limit (REL) is proposed as a new evaluation index of the machining quality, which realizes quantitative evaluation of machining quality and is applied to optimization of machining quality. Aiming at the disordering of the measurement data and lack of effective analysis in actual production of engineering, a Machining Quality Evaluation and Optimization software (MQEO) is designed and developed with REL as the evaluation index. And the multi-factor analysis of variance is used to find out the main factor influencing the machining quality, and propose optimization suggestions.
Effect of double quenching process and tempering temperature on the microstructure and mechanical properties of a High Strength Low Alloy Steel
Authors: Zhu Fei , Luo Xiaobing , Chai Feng , Yang Caifu , Zhang Zhengyan 
Published: March 2020

Abstract
Effect of double quenching process and tempering temperature on the microstructure and mechanical properties of a High Strength Low Alloy Steel was investigated by OM, SEM, TEM methods and so on. The results demonstrated that double quenching process could refine prior austenite grains (PAG) effectively and the average diameter of PAG was refined by 5.4μm compared with single quenching process. Yield strength and tensile strength of double quenching and tempered specimens (QQT) and single quenching and tempering specimens (QT) exhibited similar variation trend with tempering temperature. Yield strength increased monotonously with increasing tempering temperatures. However, tensile strength descended at first and then recovered with rising tempering temperature. QQT specimens displayed greatly improved impact absorbing energy at -80°C but a little improved yield strength compared with QT specimens owing to the refined microstructure caused by double quenching process. Considerable austenite formed during holding process of tempering (670°C) in which elements of nickel, manganese and copper aggregated. And some of the aforementioned austenite transformed to hard phases (secondary Martensite and M-A islands) and some are retained as islands of austenite after water cooling. The presence of hard phases is the main reason for recovering tensile strength and deteriorating impact absorbing energy at -80°C.
Experimental study on dielectric properties of SiC material and temperature distribution of rubber materials containing silicon carbide coated metal via microwave heating
Authors: Li Qingling , Ye Ruzhou , Jin Ruizhu , Chen Hailong , Xia Guangzhen , Li Tao 
Published: March 2020

Abstract
Microwave heating has not been widely used in the heating process of the rubber products containing metal skeleton, because the metal reflects microwaves that leads to a large amount of heat in the metal-rubber contact surface due to the skin effect and the eddy current effect in the microwave field. This experiment investigated the effects of microwave frequency and temperature on the dielectric parameters of silicon carbide coating materials. The microwave heating process of rubber composites containing metal skeleton coate d with SiC mixture was also investigated experimentally. The results are as follows: As the microwave frequency increases, the dielectric parameters of the SiC coating material increase slowly first, when the frequency reaches a certain point, the dielectric parameters increase sharply to a maximum after that fall rapidly; Based on microwave frequency of 2450MHz, the dielectric constants of three different compositions of SiC coating materials decrease with increasing temperature; The temperature of the rubber composite containing the steel wire coated with the SiC is lower than the rubber composite containing the steel wire naked. The larger the content of SiC, the more uniform the temperature distribution, and the more effective the weakening of the sparks phenomenon caused by the skin effect.
Viscosity and rheological behavior of aqueous nanofluids containing TiO2 nanoparticles
Authors: Kong Linghui , Li Guixun , Yang Yong , Zheng Jun 
Published: March 2020

Abstract
Nanofluids containing different concentration of TiO2 nanoparticles were prepared. Their rheological behavior was investigated on LVDV III ultra-programmable Brookfield viscometer at different temperature and particles loading. Results show that rheological behavior of nanofluids is determinated by the concentration of TiO2 in base fluid, nanofluids shows good Newton behavior when nanoparticles loading is low, while shear thinning becomes evident when the volume fraction of particles was continuously increased. Besides, viscosity of nanofluids shows strong nanoparticles loading and temperature dependence, but quite differs from classical viscosity model, as the theory models severely underestimate the value of viscosity, which is attributed to electroviscous effect of nanoparticles in base fluids. The primary electroviscous coefficient is evaluated at different pH values. Modified primary electroviscous coefficient is in good agreement with Booth’s theory.
The promotional effect of sodium chloride on thermophysical properties of nitrate
Authors: Wang Chenge , Li Yan , You Gouyou , Zhu Qunzhi 
Published: March 2020

Abstract
Nitrate are the excellent phase change materials (PCMs) for thermal energy storage. Nitrate has no phase separation during phase transition and has good chemical stability. The present study concentrates on improving the latent heat of phase change and of heat absorption rate by adding various proportions NaCl into the nitrate/mixed nitrate. Sodium nitrate and sodium chloride were selected to prepare binary mixture by static melting method. The melting point and latent heat of phase change were measured by differential scanning calorimetry (DSC). Compared with pure sodium nitrate, the melting point and solidification point of binary molten salt containing 5% NaCl are reduced by 9°Cand 16°C, respectively. On the other hand, the latent heat of melting and solidification increased by 12J/g and 10J/g, respectively.
Synthesize of NiMoO4/Reduced Graphene Oxide Composite as High Capacity Anode Material for Lithium Ion Batteries
Authors: Li Lin , Cheng Jinsong , Liu Qing , Wang Ting , Zhao Rongfei 
Published: March 2020

Abstract
NiMoO4/Reduced graphene oxide (NiMoO4/rGO) composites material was prepared by a hydrothermal method. When used as a anode material for lithium-ion batteries, the NiMoO4/rGO composites show superior rate capability and excellent cycling stability. A reversible capacity of approximately 808 mAh g–1 is retained even after 100 cycles at 100 mA g–1. It is believed that the surface modification by graphene nanosheets contribute to the improved kinetics of lithium-ion diffusion, excellent structural stability and superior electrochemical performance.