bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fast and coarse registration of point cloud in ICP three-dimensional space
Authors: Ren Xiaokang , Zhang Mai , Wang Wenqiao , Mao Xuetao , Ren Jie 
Published: March 2020

Abstract
The traditional ICP algorithm has a large amount of computation in the process of searching for corresponding points[1].This is the bottleneck of the traditional ICP algorithm[2],and the standard ICP is looking for the corresponding point[3].The point where the Euclidean distance is closest is the corresponding point[4].Unreasonable,there will be a certain number of wrong points[5].In order to improve the accuracy and speed of 3D point cloud data registration,this paper combines the coarse registration of the center of the box and the circular domain search method of the rasterized small cube to further improve and optimize the traditional ICP algorithm[6].
A fourth-order 9-point finite difference method for the Helmholtz equation
Authors: Chen Jianjun , Cheng D S , Jie Rong , Zhu Xiaoling 
Published: March 2020

Abstract
In this paper, we develop a new fourth-order 9-point finite difference scheme for solving the Helmholtz equation. The central fourth-order difference scheme is used to discretize the second-order derivative, and the matched interface and boundary (MIB) method is employed to deal with the resulting boundary problem. For the discretization of the zero-order term, a weighted average method is designed by utilizing all of the 9 points, and the weight parameters are determined by minimizing the numerical dispersion. The new method is simple and efficient in suppressing the numerical dispersion. Finally, numerical examples are presented to illustrate the numerical convergence and effectiveness of the new scheme.
Research on Digital Image Correlation Method used for Strain Detection
Authors: Tao Li , Yang Zaihua , Yi Wangmin , Duan Chenxu , Zhang Yuhang , Wang Ziwen 
Published: March 2020

Abstract
Strain measurement is an important means to study structural strength. Strain gauge measurement method is widely used in different industry area. The strain gauge should be stuck on the surface of the measured object to detect displacement or strain. As the measurement mode is contact measurement, strain gauge can have a certain impact on the measured object itself. So research of strain detection is increasingly moving from contact method to non-contact method. In this paper, we design a digital image correlation non-contact method used for strain detection. The basic principle of image correlation method is introduced. And the image process algorithm is detailed described. A set of laser lighting hardware is especially used for object illumination to get a random distribution light spot instead of sticking target bar on object. The measurement results show that this method can detect strain with good effect and can be applied to non-contact strain detection test.
Suggestions and thoughts on improving the teaching ability of teachers in higher vocational colleges under the background of MOOC
Authors: Guangyong Liu , Ruolan LI 
Published: March 2020

Abstract
This paper elaborates on the emergence and development of MOOC, investigates and researches the current situation of teachers’ MOOC teaching in higher vocational colleges, deeply analyzes the role of MOOC in promoting the professional knowledge structure and professional quality of teachers in higher vocational colleges, and puts forward some Suggestions and thoughts on the current situation of teachers’ teaching ability in terms of using MOOC in higher vocational colleges. The first section in your paper
Unmanned Vehicle Route Planning Based on Improved Artificial Potential Field Method
Authors: Zha Min , Wang Zhiwen , Feng Jing , Cao Xinliang 
Published: March 2020

Abstract
Aiming at the defect of target unreachable and local minimum in the unmanned path planning by traditional artificial potential field method, an improved artificial potential field method is proposed in this paper. By rotating the repulsive force by a certain angle, the unmanned vehicle jumps out of the local minimum point, and adopts the safety distance and the adjustment factor in the repulsive field to solve the problem of the target unreachability, and also ensures the safety of unmanned vehicles during obstacle avoidance. The experimental results show that the improved artificial potential field method can better achieve the safe obstacle avoidance of driverless vehicles and effectively plan the optimal target path.
The application of artificial intelligence in stock investment
Authors: Gao Zhao 
Published: March 2020

Abstract
This paper mainly studies the application of artificial intelligence and machine learning in the field of stock investment. The principles and characteristics of KNN, k-Means, bisecting k-Means, and ANN algorithms are studied to compare the effects, similarities and differences of different algorithms. The algorithms are implemented through Python programs for stock analysis. According to the P/E ratio, dividend rate, fixed asset turnover rate, gross profit margin and other indicators of each stock, the stocks are classified and clustered to predict the stock development prospects and provide reference for selecting appropriate investment strategies.
Design of Virtual Home Decoration Indoor Scene Automatic Reconstruction System Based on Three-dimensional Vision
Authors: Libin Tong 
Published: March 2020

Abstract
Because indoor scenes are structured, even small errors can lead to obvious visual differences in image-based indoor scene automatic reconstruction. Aiming at the specific requirements of automatic indoor scene reconstruction in virtual home decoration, this paper designs an automatic indoor scene reconstruction system based on image, including the calibration of image acquisition platform, the matching and reconstruction of feature features and feature lines, and the fusion of reconstruction results from multiple perspectives. In the overall optimization of reconstruction results, feature features and feature lines are integrated. The system is convenient and practical, and can get better reconstruction results.
FM-to-AM Effect Removal Technology of Pulse Waveform Data Based on Deep Learning
Authors: Tian Zhiyu , Zhang Hao , Chu Xinkun , Qin Ying , Zhang Qing , Geng Yuanchao , Huang Xiaoxia , Wang Wenyi , Wang Fang 
Published: March 2020

Abstract
In the process of laser pulse transmission, amplification and sampling measurement, the amplitude-to-frequency modulation (FM-to-AM) effect caused by spectral distortion leads to peak oscillation of waveform measurement data, which is difficult to be completely eliminated by improving measurement methods. In this paper, a method of removing the FM-to AM effect of pulse waveform data based on deep learning is proposed. After the original waveform data is de-modulated, the accuracy of waveform prediction is obviously improved. This technology can precisely remove modulated signals while retaining the key features of original data, and can well deal with various complex waveform data.
Research on binary vulnerability mining technology based on control flow integrity detection
Authors: Zhang Bing 
Published: March 2020

Abstract
Aiming at the problem of strong pertinence and poor universality of existing binary vulnerability detection methods, binary vulnerability automatic mining method based on control flow integrity detection is proposed. In view of the common binary vulnerabilities, such as stack overflow, heap overflow, function pointer tampering, structured exception handling attacks and so on, they can be unified and abstracted to destroy the integrity of control flow. This method first obtains all legitimate control flow transfers through static binary analysis, then uses symbol execution engine to explore all feasible paths, and injects constraints at control flow transfers. Finally, by solving constraints to determine whether there is a vulnerability that destroys the integrity of control flow. If there is, it automatically generates input that can trigger vulnerabilities.
Security mechanism building for big data based on Trusted Computing
Authors: Hai-yan Lv , Li-Jun Zhou , Yuan Zhao 
Published: March 2020

Abstract
In view of the security risks faced by large data in the process of collection, storage, transmission and use, the security threats faced by large data in these processes are analyzed. The characteristics of trusted computing and its advantages in information security protection are analyzed. On this basis, a secure storage, transmission and authentication mechanism of large data based on trusted computing is proposed to achieve active network defense and ensure that large data can be controlled, trusted and manageable in the process of storage, transmission and authentication.
Short-term electricity price forecast based on long short-term memory neural network
Authors: Lihong Dong , Qian Xie 
Published: March 2020

Abstract
Effective short-term electricity price forecasting is of great significance to reduce bidding risk and obtain stable income. In order to predict the timing price, this paper proposes a short-term electricity price forecasting model based on Long Short-Term Memory (LSTM) neural network. The model uses pauta criterion and Lagrange interpolation polynomial to process the historical data of electricity price, normalize the obtained data and input it into the LSTM network layer, using the Mean Squared Error as the loss function to measure the model forecasting. The effect is based on the loss function, and the weight coefficient of the LSTM neural network is updated by the Adam algorithm. Finally, the timing of the LSTM neural network is predicted. Using the real-time data of the California power market and the real-time data of the US PJM power market, it is proved that the accuracy of this method is higher than that of BP neural network, Cart regression tree and polynomial regression.
Modulation Recognition of 5G Signals Based on AlexNet Convolutional Neural Network
Authors: Zhang Qing , Hu Guobing , Zhao Pinjiao , Yang Li 
Published: March 2020

Abstract
Aiming at the problem that signal modulation recognition under a non-cooperative condition requires substantial a priori information of the signal and a complex artificial selection of the features, this paper proposes a modulation recognition method for the 5th-generation (5G) signal modulation based on the AlexNet convolutional neural network. For the five commonly used 5G signals (3GPP R15 protocol recommendations) of π/2-BPSK, QPSK, 16QAM, 64QAM, and 256QAM, the constellation is selected as input feature of the AlexNet network to construct the recognition classification algorithm. The simulation results show that the average recognition accuracy of the five commonly used 5G signals is up to 90% under a 15 dB signal-to-noise ratio (SNR), an improved performance compared with that of the existing recognition algorithms based on signal scatter plots.
A Practical Method to Improve Absolute Positioning Accuracy of Industrial Robot
Authors: Shi Guosong , Zhao Shuying , Hu Baolong 
Published: March 2020

Abstract
Industrial robots had been wildly used in modern factory. While the high repeatability of industrial robots can address most of need of process, their low absolute positioning ability cannot reach the demands of some high precision tasks. An example is if the action of the robot relies on the real-time sampled data rather than the simulated data. The low absolute positioning ability of the robot will cause working performance far different away from expectation. For improving the absolute positioning accuracy as long as compile with the programs of automation integration projects, a practical general compensation method has been advanced. Our method also can be a compensation frame which can absorb new better model in the future. One application of this method in a project verifies the achievement of this method.
A Survey on Image Style Transfer Approaches Using Deep Learning
Authors: Zhao Changshen 
Published: March 2020

Abstract
Image style transfer has been an important branch of computer vision and image processing. Inspired by the development of deep learning, applications of Convolutional Neural Networks (CNNs) in recomposing content and style of two separated images were proven to be effective by the recent works of image style transfer. This paper provides a survey of image style transfer, which focuses on methods which using deep learning. In addition, this paper also provides information of methods without deep learning, and seeks their points of innovation. In this paper, all references are published ranging from 2001 to 2017, which presents an overview of progress in this realm over the past two decades.
FMN: Two-stage web image fine-grained text detection method based on full convolutional network
Authors: Fan Jiajie , Wang Yongli , Sun Shurong 
Published: March 2020

Abstract
In the Internet, there are large-scale web images with different image sizes and different resolutions. In order to accurately and efficiently identify fine-grained texts in web images, this paper uses FCN to support the ability to semantically segment images, and treats text and background as different detection targets. A two-stage web image fine-grained text detection method FMN is proposed. For the problem that the output result of the whole convolutional network is not fine enough, the MSER+NMS algorithm is used to extract the fine-grained features of the FCN output, and the ellipse fitting method is introduced to detect the text. Features such as character tilt and morphological differences ultimately result in text detection of the image. Compared with the existing methods, the FMN algorithm proposed in this paper has achieved certain improvement in detail resolution and precision.
Construction of vehicle driving cycle in Fuzhou
Authors: Zou Sijia , Zhu Xinru , Xu Linjun , Zhu He 
Published: March 2020

Abstract
The current driving cycle in Europe is not consistent with China’s actual road conditions, and the traffic conditions in different cities of China also are discrepant. Therefore, it is increasingly urgent to build corresponding driving cycle based on the driving data of each city. In this paper, based on noise reduction pretreatment of vehicle driving data in three areas of Fuzhou city, K-means clustering is carried out on the first five principal components of the extracted kinematic segments obtained by principal component analysis. Then, three types of kinematic segments, which represent relatively smooth traffic, traffic congestion and smooth traffic, are obtained respectively. So the vehicle driving cycle is constructed, and the correlation coefficient between it and the sampling population for constructing is calculated. Finally, the error analysis method and correlation coefficient are used to verify the constructed driving cycle that is rational and representative.
Analysis of the Influence of Digital Media Technology on Music Communication
Authors: Wenting Kong 
Published: March 2020

Abstract
Under the condition of digitalization, mass media, as a carrier, has come into people’s vision, and has exerted an important influence on music communication. The media industry has grown steadily, and the relationship between mass media and music has gradually become complex and close. With the continuous development of the media industry, it has an impact on the production, dissemination and popularity of music art. This paper mainly discusses the role of the mass media and the influence of the mass media on music communication, and analyses the ways to improve the mass media on music communication.
MIHNet: Combining N-gram, Sequential and Global Information for Text Classification
Authors: Song Yingxin 
Published: March 2020

Abstract
Pre-training language model has achieved amazing results in many NLP tasks. In Particular, BERT (Bidirectional Encoder Representations from Transformers) create a new era in NLP tasks. Despite the success, these model perform well at Global Information but weak on n-gram and Sequential information. In this paper, we conduct exhaustive experiments of classical text classification models upon BERT in text classification task and provide a general guide for BERT+ models. Finally, we propose a new text classification model called MIHNet (Multi-dimension Information Integration using Highway network), which integrates Global, n-gram and Sequential information together and get a better performance. Notably, our model obtains new state-of-the-art results on eight widely-studied text classification datasets.
Clustering Analysis of Urban Fabric Detection Based on Mobile Traffic Data
Authors: Liu Chuangfei , Liu Chang , Liu Fuqiang , Hu Jianyao 
Published: March 2020

Abstract
The rapid development of city makes it complicated to analyse urban structure. It’s difficult to learn city ecosystem using traditional methods including interview and survey. A city generates lots of data every day, which could tell city’s dynamic fabric. The moving patterns of citizens could be illustrated through analysing traffic data on their mobile phones, since those who carry a mobile phone possess a large percentage of the urban population. Thus, we can get the designated urban area’s functions through the moving pattern analysis. In this paper, we explore the fabric of city using cluster analysis based on deep learning with mobile phone communication data. We get the inspiration from image processing and build communication snapshot map to represent each region. After extracting features using deep learning method, we use unsupervised learning to find similar regions of the city. The clustering analysis result is examined by the ground truth data.
Statistical downscaling of GCM using kernel support vector regression for rainfall prediction in Bireuen district
Authors: Sanusi  , Husna J 
Published: March 2020

Abstract
Global climate phenomena, including El-Nino and Southern Oscillation, Indian Ocean Dipole and Madden Julian Oscillation, have affected rainfall behavior in Indonesia. This study attempts to model a statistical downscaling of the General Circulation Model (GCM), which is usually used as learning data for the prediction of climate change, using Support Vector Regression (SVR) to predict rainfall during the dry season in Bireuen, Aceh Province. Data consists of observational rainfall data at 7 (seven) weather stations and 10 models of 7x7grid-scale hindcasts of GCM data collected within 24 years (1990-2013). The RBF kernel was found to produce the best performance (correlation value: 0.828; RMSE: 24.035) compared to the linear (correlation value: 0.538; RMSE: 27.207) and polynomial kernel (correlation value: 0.639; RMSE: 25.584). Among GCM downscaling output, CMC1-CanCM3 was considered to be the best model for rainfall prediction in Bireun District, particularly in Peudada and Gandapura regions. This model is recommended to be used as a rainfall forecasting tool during drought and would help in optimizing the arrangement of cropping strategy in Bireun area to prevent possible crop failure.
Analysis of the needs of science, technology, engineering and mathematics (STEM) learning instruments on the derivative topic
Authors: Husna R , Johar R , Usman  
Published: March 2020

Abstract
STEM (Science, Technology, Engineering, and Mathematics) is an approach which combines or fuses science, technology, engineering, and mathematics into education program to improve students’ learning potential. STEM is started to be disseminated by the government to provide a push for the realization of Industrial Revolution 4.0 in this century. The purpose of this study is to describe the necessity of STEM learning instruments on the derivative topic. This study is part of the developmental research on the preliminary stage, which is the needs analysis. Three of eleventh-grade math teachers were invited to participate in this study. The questionnaire about various aspects of the needs analysis was given to the teachers. The study results found that the teachers agree to develop STEM learning instruments on the derivative topic and are interested in examining the instruments if they are accessible for them.
Need analysis for developing Geogebra assisted mathematics learning tools to improve mathematical understanding of senior high school
Authors: Handayani V , Usman  , Ansari BI 
Published: March 2020

Abstract
The aim of this study is to analyze the initial needs of the development of GeoGebra assisted mathematics learning tools to improve students’ mathematical understanding abilities. The population of this study were students from SMA Negeri 6 Banda Aceh and Mathematics teachers from four schools in Banda Aceh and Tanjung Pura. Purposive sampling method was used to take the sample. Data collection were collected by observation and interview. All the data were analyzed descriptively. The findings revealed that mathematics learning in 11th grade high school students can be developed with the help of GeoGebra. Students feel interested and motivated to take part in assisting GeoGebra. It also found that GeoGebra assisted mathematics learning tools are needed to improve students’ mathematical understanding because the students’ mathematical understanding ability is still low.
The quantitative reasoning ability of high school students
Authors: Hafiza N , Usman  , Anwar  
Published: March 2020

Abstract
Quantitative reasoning is a mental activity that emphasizes drawing conclusions based on quantitative data or information. Students are often wrong in solving quantitative reasoning problems. Students do not understand very well the relationship between the information provided with the questions in the problem. The solution to this problem is to investigate students’ quantitative reasoning ability. This study aims to find out the activities of students’ quantitative reasoning abilities. This research uses a qualitative approach. Twenty-three high school students were involved in this study. Data is collected by giving tests in the form of quantitative reasoning ability questions. After the test, a brief interview was conducted with some students. The results of this study indicate that students still have difficulties in answering questions in the form of quantitative reasoning abilities. Therefore serious attention is needed to improve students’ quantitative reasoning abilities.
Development of student worksheet to improve mathematical representation ability using realistic mathematics approach assisted by GeoGebra software
Authors: Nurlisna  , Anwar  , Subianto M 
Published: March 2020

Abstract
Nowadays, student worksheet is used with a lack of concern on students’ mathematical representation ability. In fact, it is essential to encourage students to deliver their mathematical ideas in various ways for broader perspectives of mathematical problem-solving. This study aimed to generate a valid, practical, and effective student worksheet to improve students’ mathematical representation ability with a Realistic Mathematics approach assisted by GeoGebra Software. The research is developmental research using the ADDIE model, which includes Analysis, Design, Development, Implementation, and Evaluation. The instruments used in this study included: assessment sheets (to measure the validity of the worksheet); student questionnaire; teacher questionnaire; observation sheets (to measure practicality of the worksheet implementation); and mathematical representation ability test (to measure the effectiveness of worksheet implementation). This study created a student worksheet for four meetings. The student worksheet met valid criteria, with an average score of 4.25. The practical criteria were indicated by student and teacher questionnaires, with an average of 4.25 and 4.4, respectively; and the learning implementation (81.72%). Furthermore, the learning material fulfilled the effectiveness criteria with the percentage of students’ learning understanding was 85.71%.