bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design of Embedded Intelligent Car Control System Based on Android
Authors: Liu Jianying 
Published: November 2020

Abstract
With the rapid development of artificial intelligence technology, Unmanned intelligent vehicle technology, more and more people pay attention to it, it will change the way people live and travel, it can be applied to all aspects of urban construction and social development. Aiming at the instability of the intelligent car in the process of automatic driving, an embedded intelligent car control system based on Android is designed. Cortex-A9 is the core of the main control platform of the system, connect seven function modules, and through the data receiving module of the controller, it is connected with the Android application control system of the host computer, each function module can work independently and cooperate with each other, realize the automatic control of intelligent car, the intelligent car can move forward by itself, identify the path, grab the goods, patrol the line, avoid obstacles, and can run smoothly and quickly, at the same time, it can accept the manual control of Android interface.
Application of virtual reality technology in intelligent cold chain logistics system
Authors: Yao Li Juan , Li Jie , Han Jing Jing 
Published: November 2020

Abstract
The origin of virtual reality technology can be traced back to the 1950s decades ago. In recent years, with the rapid improvement of computer graphics processing, computing ability and other technologies, virtual reality technology and equipment began to mature. Starting from the application and development of virtual reality technology in various fields, this paper analyzes the basic application process of virtual reality technology, and describes the value and content of its application in the intelligent system of cold chain logistics.
Prediction analysis of shield vertical attitude based on GRU
Authors: Li Liang , Hu Changming , Hou Yajun 
Published: November 2020

Abstract
To solve the active control of the shield vertical attitude, this paper established the GRU model to realize the prediction of the pitch angle, which can reflect the shield vertical attitude. Firstly, the tunneling parameters, geological parameters and geometric parameters were selected as the influencing factors sets, and a series of data pre-processing strategies were established. And then, the GRU model was established, mainly including the division of training data, the optimization of hyperparameters, the determination of the objective function and optimization algorithm. Eventually, the shield interval from Chengdu was used to verify the new model. As a result, compared with the BP model and LSTM model, it could be found that the MGRU model had higher accuracy. This study could provide references for the prediction and help the engineers to realize the active control in the field of the shield attitude.
A white-box impersonation attack on the FaceID system in the real world
Authors: Guo Lu , Zhang Hua 
Published: November 2020

Abstract
The arcface model maximizes the classification boundary in the angular space, and is one type of the best models in the current face recognition model. We propose a mask sticker attack method to realize the impersonation attack of arcface model. The method specifically uses a parabolic transformation to simulate the bending situation of a sticker on a mask, and uses a multi-stage PGD attack to generate a adversarial sticker. Finally, the adversarial sticker is attached to the mask worn by the attacker to perform an impersonation attack. On targets of different skin colors, ages and genders, the method proposed in this article has an attack success rate of 0.65, and the final cosine similarity between attacker with mask sticker and target can reach about 0.5-0.7. In addition, we discuss the effect of sticker location and size on attack effect, as well as the generalization of this method on other models.
Data Augmentation For Chinese Text Classification Using Back-Translation
Authors: Ma Jun , Li Langlang 
Published: November 2020

Abstract
Text classification is a basic task in natural language processing. When the amount of data is insufficient, the classification accuracy will be greatly affected. We propose to use the back-translation method to expand three Chinese data sets used for text classification, and then train and predict the data sets through deep learning classification model. The results prove that using back-translation to expand the data is particularly helpful on a smaller dataset, it also can reduce the unbalanced distribution of samples and improve the classification performance.
Research on the characteristics of the growth risk sources of new ventures based on Immune Theory
Authors: Yang Bo , Yao Yuqi 
Published: November 2020

Abstract
Under the background of the national strategy of “mass innovation and entrepreneurship” and the information age, it is very important to use the artificial immune theory to characterize the growth risk sources of new ventures and make them become the quantitative index features available for the model. It can lay the foundation for the construction of risk identification model of new ventures. First of all, through the in-depth analysis of the characteristics of the growth risk of the new venture, the source of the growth risk is defined according to the classification of the growth risk of the new venture; then, through combing and summarizing the relevant research at home and abroad, this paper analyzes the influencing factors of R & D risk source, product risk source, management risk source and social network risk source, verifies and selects through empirical research, and constructs the identification index system on this basis; finally, the growth risk characteristic structure of new ventures based on artificial immunity is constructed, and risk identification indicators are engineered to transform qualitative indicators into quantitative indicators for model experiments. It provides a basic reference for the establishment of the new enterprise growth risk identification model, so as to effectively meet the in-depth decision support, information mining and discovery in the growth process of the new enterprise, thereby promoting the stable and healthy growth of the new enterprise.
Research and implementation of no-tillage information monitoring strategy based on Hadoop
Authors: Yang Hongwei , Xue Fucheng , Gao Sheng , Zhu Hongmei , Li Li 
Published: November 2020

Abstract
Based on the Hadoop, this paper uses linear regression algorithm to study the intelligent monitoring strategy of no-tillage information. In this paper, the regional computers in this system use Flume and Kafka to transfer large amounts of farming information from local node databases to HDFS (Hadoop Distributed File System), then process this data with Spark, whatever, the system uses time series linear regression algorithm to predict the fault of no-tillage. Compared with the no-tillage designed before, the system described in this paper not only reduces time delay but also improves the fault tolerance of data storage and the system stability. Field experiments have proved that the research of this strategy has an accuracy rate of up to 90%, which reduces the huge economic losses caused by machine failure.
Research on GPU-based Real-time Smoke Simulation Volume Rendering Algorithm
Authors: Deng Dingsheng 
Published: November 2020

Abstract
With the continuous development of the current film industry and animation industry, in the current smoke simulation, based on the use of GPU, real-time smoke simulation can be realized, so that real-time smoke simulation can be enhanced to achieve more smoke simulation. The ideal effect makes people have a better visual experience. The purpose of this article is to study the GPU-based real-time smoke simulation volume rendering algorithm. This paper proposes a method of setting different transfer functions based on different positions of the imaging plane. By setting different transfer functions for different positions of the imaging plane, different tissue structures can be drawn at one time for comparison and observation. However, setting different transfer functions based on the coordinate information of the imaging plane cannot fully display the volume data information. This paper implements a method of setting different transfer functions based on different spatial positions in the object space, and sets different transfer function schemes for the region of interest in the object space. Highlight, rotate or zoom the drawn object to observe the structure of the region of interest at any angle and size. In addition, this paper realizes the rapid volume cutting of volume rendering results, which is convenient for observing the organization structure information of the occluded position or designated area. Experimental results show that the algorithm in this paper can be used to control the random motion of smoke in three-dimensional space in virtual and real scenes, and can complete various interactive simulations of smoke and obstacles.
Rule Extraction Algorithm of Ordered Decision Information System
Authors: Mo Jing-Lan 
Published: November 2020

Abstract
In ordered decision information system, an improved LEM2 algorithm (DRI-LEM2) is proposed based on the generalized decision function, aiming at the low efficiency and low quality of LEM2 algorithm in extracting rules. The algorithm computes the candidate attribution-value pair set according to the generalized decision function, and removes redundant attribution-value pair from the candidate attribution-value pair set to gradually reduce the size of the property-value pair set. Through UCI experiments, it is proved that the improved LEM2 algorithm (DRI-LEM2) based on dominance relations can effectively improve the efficiency and quality of rules extraction.
A review of research on detection of fake commodity reviews
Authors: Tang Haoxing , Cao Hui 
Published: November 2020

Abstract
With the development of the Internet era, more and more consumers are shopping online. Product reviews can influence consumers’ consumption decisions. Fake review detection is a valuable research. From the perspective of research data content, it can be divided into two categories: review content based and review metadata; from the perspective of fake review detection method, it can be divided into four categories: rule-based, graph-based model, machine-learning based and deep learning based. Common data sets are also introduced.
Crowd density estimation method based on floor area
Authors: Feng Fujian , Liu Shuang , Pan Yongzheng , He Xin , Wei Jiayin , Wang Lin 
Published: November 2020

Abstract
Crowd density estimation is a hot issue in the analysis of crowd abnormal behavior. At present, the crowd density is calculated by the ratio of population and area. However, these methods ignore the influence of monitoring perspective and local crowd distribution in the selection and processing of area. In this paper, we propose a crowd density estimation method based on the floor area, which can accurately estimate the density of low and medium density crowd. Based on crowd density estimation, we propose a new division method of crowd density rank, which can effectively judge the crowd density rank at each moment. The proposed method is executed by the data sets of Mall, Smarty City and USCD. The experimental results show that proposed method can effectively estimate the crowd density value in the low and medium density crowd.
Effect of natural background on detection threshold of radioactive material detection system
Authors: Li Yan , Zhao Hongtao , Li Gang , Zhao Xiaowen , Wu Peng 
Published: November 2020

Abstract
In this paper, the relationship between detection limit and error probability is studied based on the statistical law of radioactivity. The conversion method between the lowest detectable radioactivity count and the activity can be obtained, which provides the basis for setting the alarm threshold reasonably.
Multi-sensor scheduling method based on Cuckoo and Particle Swarm optimization algorithm
Authors: Wei Wenfeng , Liu Changyun , Wang Chuang , Li Junxia 
Published: November 2020

Abstract
In order to solve the problem that particle swarm optimization (PSO) tends to fall into the local optimal solution and the convergence speed is slow when solving multi-sensor resource scheduling model, a cuckoo particle swarm optimization (CPSO) algorithm is proposed on the basis of PSO. On the basis of target tracking model, the multi-sensor scheduling model is established. Then the LEVY flight was introduced from cuckoo algorithm into particle swarm algorithm, the algorithm can jump out of the local optimal solution as soon as possible and improve the convergence speed and accuracy of the algorithm. The simulation results show that the improved algorithm is effectively improved in terms of convergence speed and accuracy, and is applied to the solution of sensor scheduling model to further enhance the optimization ability and achieve good results.
Research on Landscape Design Based on Digital Technology
Authors: Jun Yan , Xian Shan Xiao 
Published: November 2020

Abstract
Firstly, this paper briefly analyzes the concept and development of digital technology. Then it deeply analyzes the application research of digital technology, including digital projection, VR technology and mixed reality. Finally, it expounds the application research of digital technology in landscape design, and puts forward the design strategy of improving the projection technology and VR virtual reality technology in digital technology from the current situation of landscape design development in China, so as to promote the integration of digital technology in landscape design development.
A surface defect identification method based on improved threshold segmentation algorithm
Authors: Feng Xinglong , Gao Xianwen , Luo Ling 
Published: November 2020

Abstract
For enterprises, defect detection is very important because it is related to the quality of products produced by enterprises. With the development of machine vision, accurate analysis of image data benefits defect detection. In an enterprise that produces electronic cigarettes, professional and technical personnel used to detect a defect in a workpiece by using manual testing. The defect detection rate of this method is only 95%, and the efficiency is low. We use an improved threshold segmentation method to solve this problem in this paper and we have achieved success. Compared with typical methods, the accuracy of our proposed algorithm reaches over 99% and at the same time, the detection efficiency has been improved by more than 50%. Our method also has the advantage of simplicity, practicality and low cost.
Research and application of improved K-means based on MapReduce
Authors: Wang Hongqin , Wang Hongxia , Jiang Li , Pan Zhengjun 
Published: November 2020

Abstract
With the development of big data, the traditional data mining clustering algorithm K-Means is inefficient and has poor scalability in dealing with massive data. MapReduce on the Hadoop platform was used to realize the parallel processing of the K-Means algorithm, the performance of the algorithm was tested by experiments. The results show that the improved K-Means algorithm has good parallel expansion capability, high efficiency, and great potential when processing big data mining. The algorithm is applied to the big data processing of customer consumption in a restaurant chain, and the effectiveness of the algorithm is verified, which can better serve the decision of restaurant.
Real Terrain Simulation based on Point Cloud of Water Area and Land Area
Authors: Ma Haibo 
Published: November 2020

Abstract
The joint simulation of land and water integration plays an increasingly important role in surveying and mapping, water conservancy, and land resources supervision. Based on the theory of 3D coordinate transformation, this paper discusses the application of Rodrigues’s matrix and indirect adjustment model in the iterative calculation of point cloud registration parameters. In this method, anti-symmetric matrix elements are used to form a rotation matrix, which avoids the complex trigonometric function operation of traditional methods, and improves the efficiency of calculation while ensuring the accuracy, and can easily calculate the initial value of parameters. Experimental results demonstrate that the proposed method has the advantages of high accuracy, robustness, stability, and reliability.
Exploration of the digital display design method of ancient coconut shell hill archaeological site
Authors: Cai Pei , Zhang Xiaopu , Yu Yue 
Published: November 2020

Abstract
With the development of science and computer technology, digital display design breaks through geographical and time and space constraints, not only can realize the restoration and protection of ancient coconut shell mound ruins, at the same time, the rapid spread of ancient coconut shell mound site culture was realized. This paper analyze the current situation of the ancient coconut shell mound site display and discover the core content of the ancient coconut shell mound site display to explore a solution to the digital display design of the ancient coconut shell mound site. The digital display design is conducive to the cultural information dissemination of the ancient coconut shell mound site, and improves the interactive experience and satisfaction of visitors; it aims to play a certain guiding role for the ancient coconut shell mound site to truly and completely explain the value of the site and spread the connotation of the site.
Monitoring of rice lodging using Sentinel-1 data
Authors: Wang Jinning , Li Kun , Shao Yun , Wang Zhiyong 
Published: November 2020

Abstract
Synthetic Aperture Radar (SAR) can monitor rice regardless of time and weather condition, and the ability of crop lodge monitoring has been demonstrated by previous studies. However, there have no study about monitoring of rice lodging using satellite-based SAR. In this study, we extracted backscatter coefficient (BC) and H/A/Alpha polarimetric parameters from dual-polarized Sentinel-1 SAR data, and a lodging sensitivity factor γ was constructed for selecting optimal radar feature parameter (ORFP) which is highly sensitivity to lodging rice. Then, the decision tree classification method was used with multiple ORFPs to classify the healthy and lodging rice plots. We firstly acquired shape of rice region using Sentinel-2 image and Maximum likelihood classification method for eliminating the influence of other features before classification of lodging rice. The result showed that the overall accuracy of 87.18% is achieved with the combination of ORFPs and decision tree.
Correlation inversion detection algorithm and imaging simulation of wood defects focused by stress signal based on Symlets wavelet
Authors: Zeng Bin , Li Xuan , Mao Zhenwei 
Published: November 2020

Abstract
Aiming at the problems of traditional wood natural defect detection methods such as difficult to remove interference signals, weak signal enhancement, and low defect detection accuracy, a wood natural defect detection method based on Symlets wavelet and inversion reconstructed signals is proposed. First, the defect information is extracted by multi-scale slice based on Sym3 wavelet. Then, the high-frequency abnormal signal components of the defect stress after reconstruction are “focused” correlated inversion detection and analysis, and the defect information extraction process based on the inversion “focusing” is realized. Finally, the characteristic signals of natural defects in wood are effectively separated to achieve high-precision detection and three-dimensional image reconstruction of the position and shape of the defects. This method has achieved good experimental simulation results, and the recognition accuracy of wood knots and other defects is over 97%.
The Study of Locating Diseased Leaves Based on RPN in Complex Environment
Authors: Guo Yan , Zhang Jin , Su Pei , Hou Guang Hua , Deng Fang Yi 
Published: November 2020

Abstract
Plant disease is one of the major factors threatening the plant growth. In this paper, we utilize the region proposed network (RPN) to detect and locate the plant leaf based on the machine deep learning algorithm. Firstly, the original image needs to be input into convolution neural network (CNN). After several convolution and pooling operations, highly condensed image features can be obtained. Secondly, a reference boundary frame for predicting the position of an object can be obtained by sliding nine boundary frames as sliding windows on the feature map. Two neural networks are input into each boundary box to get the classification result and boundary location. Finally, with the help of non-maximum suppression algorithm (NMS), multiple boundary boxes for the same object are eliminated and only the best boundary boxes are retained. Experiments show that RPN algorithm has better performance on locating the diseased leaves in complex environment, thus reducing the influence of disease on agricultural production. At the same time, it is of great significance in economic development, ecological protection, agricultural production and other fields.
Investigation of Blind Multi-User Detection Technology
Authors: Tuo Xingyu , Zeng Wei , Zhu Zhiyuan , Guo Jing 
Published: November 2020

Abstract
Blind multi-user detection algorithm is an effective method to overcome multi-access interference and influence of near-far effect. This paper briefly describes commonly used algorithms of blind multi-user detection technology, introduces the application of the least mean square algorithm (LMS) and compares the performance with other algorithms. Aiming at the shortcomings of the LMS algorithm, the variable step size LMS algorithm and the relatively low complexity LMK algorithm based on the change of the input signal vector are studied.
Research on the University Fall Enrollment Strategies Based on Apriori Algorithm
Authors: Gao Xia , Liu Shucheng , Xu Fangqin 
Published: November 2020

Abstract
Using the historical enrollment data of colleges and universities to mine, discover the relationship between the potential data, and use the results of data mining to provide reasonable suggestions for enrollment policies, which is of great significance to college enrollment. This paper aims at the allocation of enrollment colleges and the allocation of college students in the process of autumn enrollment in colleges and universities, through the Apriori algorithm in data mining, mining historical enrollment data for 3 consecutive years after data cleaning. Based on the frequent itemsets obtained from the experimental results, a reasonable allocation of college places and student college allocation plan was made.