bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Traffic Classification of Auto Machine-learning Base on Meta-QNN
Authors: Xu Xin Yu , Huang Yong Zhong , Luo Yong Cheng , Ouyang Guige , Zhuang Haoyu 
Published: February 2021

Abstract
The traditional traffic classification task relies on feature engineering by experts with specialized knowledge. With the advent of representation-learn-based traffic classification techniques, machines learned to extract features from traffic data and classify them. In this paper, we research traffic classification technology based on representational learning and import auto-machine learning technology to solve the problems of network architecture design and parameter tuning. We also design a befitting reward function for the network architecture model. The experimental results on the USTC-TF2016 dataset and USTC-TF2016-PLUS shows that the network architecture generated by auto-machine learning technology has better training performance and classification accuracy than a traditional neural network.
Characteristic Analysis of Collision Between Drill String and Well Wall
Authors: Huang Zhihong , Wang Hanxiang , Lv Xiaoxiao , Liu Yanxin 
Published: February 2021

Abstract
In order to improve the security of drilling progress and reduce the reliability impact which caused by the collision between drill string and well wall, the finite element analysis software ADINA is used to establish a finite element model to simulate the change of drill string stress after the collision between drill string and wellbore under different factors. The results show that the existence of drilling fluid with higher viscosity, lower revolution speed and autorotation speed of drill string and larger annular space is conducive to reduce the stress value caused by collision between drill string and wellbore and improve the security and stability of the drill string.
Research on Advertising Core Business Reformation Driven by Artificial Intelligence
Authors: Lai Zhenli 
Published: February 2021

Abstract
Driven by big data and artificial intelligence technology, the core business of advertising is gradually becoming intelligent, from consumer insight to advertising information delivery and then to advertising content production. With the continuous progress of cognitive computing technology, it can be predicted that in the future, intelligent advertising will continue to step forward along the trend of “humanization” in line with human needs.
Research on Rapeseed Counting Based on Machine Vision
Authors: Peng Shunzheng , Zhao Zeying , Xiaobo Wu , Yue Yuanbin , Li Lijie , Weng Zhu , Sun Changqing 
Published: February 2021

Abstract
Thousand-kernel weight is an important agronomic parameter of rapeseed. In order to quickly realize the grain count, shorten the measurement cycle of the thousand-grain weight, in this paper, we propose a grain counting method based on image detection, by testing 300 rapeseed images, and the results showed that the detection rate reached 89.33%.
Research on Small Object Detection Algorithm Based on Feature Enhancement Module
Authors: Han Tongqiang 
Published: February 2021

Abstract
Aiming at the disadvantage of the SSD algorithm’s poor detection effect on small objects in the process of detecting objects, a detection method based on feature fusion and feature enhancement is proposed. Our model combines deep and shallow features to improve the model’s ability to detect small objects. Besides, the idea of a channel attention mechanism is introduced into the model structure to increase the feature extraction ability of the network and effectively improve the detection accuracy of the network. Experimental results show that the detection accuracy (mAP) is 2.3% h igher than that of SSD. The detection accuracy (mAP) is significantly improved compared to the current series of excellent object detection algorithms. The algorithm in this paper can detect small objects that are not detected by most SSD algorithms, which improves the average detection accuracy
Study of Wavelet Transform-based Image Fusion Methods
Authors: Cheng Kairui , Yi Longhao , Li Long , Zhutian Manzi , Sun Guang 
Published: February 2021

Abstract
The main objective of this paper is to get some basic knowledge about image fusion, the methods used for fusion, and the problems that still exist in image fusion technology, and to draw the attention of the research field to image fusion technology through a combination of theory and practice.
Design of Home Care System For Rural Elderly Based on Artificial Intelligence
Authors: Wang Liang Liang , Jia Lian Qin , Chu Feng Qin , Li Ming Xia 
Published: February 2021

Abstract
With the development of Internet of Things technology, cloud computing technology, big data technology, and artificial intelligence technology, the application of these new technologies to rural life can help rural elderly care at home. The home care system for the elderly in rural areas is based on artificial intelligence technology and uses the Internet of things technology to collect real-time environmental parameters at home and to optimize the living environment. It uses intelligent image recognition technology to judge the behavior of the elderly and interlopers and gives effective early warning and alarm. It uses wearable devices to measure the health of the elderly in real-time. It uses the man-machine dialogue program to solve the life needs of the elderly to improve the well-being of the elderly life. It integrates big data and cloud computing technology to process the collected information.
Thai Word Segmentation Based on Sequence-to-Sequence Model
Authors: Wu Hui Wen , Huang Yong Zhong , Li Can Cheng , Zhuang Haoyu , Xu Xinyu 
Published: February 2021

Abstract
Thai as a low-resource language has a large word segmentation performance improvement space. In this paper, we investigate a sequence-to-sequence model for Thai word segmentation with two different recurrent neural networks, which could transform one input sequence into another output sequence. Furthermore, we evaluate datasets in four different fields compared then with other multiple word segmentation models, and the F1 value in the encyclopedia dataset reaches 97.15%. The results show that the proposed model has superior performance and is more effective, it is worth mentioning that the expected results can be achieved even with limited data resources.
Improved Algorithm Based on The Deep Integration of Googlenet and Residual Neural Network
Authors: Huang Xuehua , Chen Weihong , Yang Wangdong 
Published: February 2021

Abstract
In this paper, we propose a new improved algorithm based on the deep integration of GoogleNet and Residual neural network and we call it GRSN. The new improved algorithm has the new advantages of multi-size and small convolution kernel in the same layer in the network and the advantage of interlayer hop connection (bypass) to reduce information loss. The algorithm is applied to the general image data set cifair10, and the experimental results are compared with that of GoogleNet, the accuracy is improved, the convergence is accelerated, and the stability is better.
An Improved Hough Transform Method for Detecting Forward Vehicle and Lane in Road
Authors: Wang Ying Sheng , Qi Yuhan , Man Yi 
Published: February 2021

Abstract
In this paper, we propose an integrated method for lane detection and vehicle detection, which tries to make a real-time analysis of vehicle video for identifying lane, detecting, and tracking forward vehicles. In lane detection, image preprocessing, line detection based on improved Hough transform, and straight-line model reconstruction are used. For vehicle detection, preprocessing, vehicle shadow merging based on the improved search algorithm, regions of interest (ROI) demarcation, lane determination, and vehicle tracking are used. The experiment results show that the time it takes to process an image is about 25ms. Additionally, the lane detection rate of vehicles driving on a structured road is approximately 98%, and the vehicle detection rate of the closest forward vehicle is approximately 81%.
Prediction of User Consumption Behavior Data Based on the Combined Model of TF-IDF and Logistic Regression
Authors: Xiao Shuwei , Tong Weiqin 
Published: February 2021

Abstract
In the era of the rapid development of computers and the Internet, e-commerce has become a part of the economy of many countries. Therefore, how to use historical data of user consumption behavior to predict user shopping intentions accurately and subsequent personalized recommendations has turned into research hotspots in the field of e-commerce. This paper conducts a basic analysis of JD’s e-commerce data based on machine learning. Specifically, this article constructs user-product matrix and product and user clustering by means of text processing and clustering, as well as implements a logistic regression classifier to predict the user’s purchase intention of products in a certain target category in the next 5 days. Based on the JD competition data set, this article has a prediction accuracy rate of 98%. This can help e-commerce companies make better decisions.
Facial Expression Recognition Based on Convolutional Neural Network
Authors: Lu Ruhua , Li Yalan , Yang Pan , Zhang Wenfen 
Published: February 2021

Abstract
At present, facial expression recognition technology is widely used in artificial intelligence, transportation, medical and other aspects, so it has important research value. Traditional facial expression recognition uses manual feature extraction method with low accuracy and weak generalization ability, which is difficult to be applied in real life. With the development of deep learning, convolution neural network appears in people’s vision. Different from traditional manual feature extraction, convolution neural network can learn image features independently, and learn more features. In addition, it has the advantage of sharing the weight with the neural network. Although convolution neural network has multiple advantages, it also has some disadvantages, especially over fitting. In this paper, the model of convolution network is improved based on the classical VGGNet according to the working principle of convolution neural network. In this new model, the number of convolution kernels is reduced in parameters, and the global average pool layer is used to replace the full connection layer in the structure, so as to reduce the degree of over fitting and decrease the operation parameters. Finally, experiments show that the accuracy, generalization and consumption of resource are enhanced in the new model. It is proposed that the new method is better than the traditional convolution network VGGNet.
An Algorithm for Target Detection Based On Aggregation Multi-Scale Feature
Authors: Chen Yishun , Dong Rongsheng 
Published: February 2021

Abstract
In order to alleviate the multi-scale problem caused by the scale change between object instances, pyramids are widely used in target detection. Although these target detectors with characteristic pyramid structure have achieved good results, they have some limitations because they simply construct characteristic pyramids according to the skeleton multi-scale pyramid structure originally used for target classification tasks. In this study, based on M2Det, a multi-scale feature with richer multilevel information is proposed to construct a more effective feature pyramid to detect targets of different scales.First, the basic features with multi-level features will be fused and extracted from the backbone.Then, the basic features are input into the M module, and the features generated by each P module are used as the features of the detection object in the form of dense connection. Finally, the attention mechanism is introduced into the L module to assemble the features with the same scale and construct a feature pyramid for target detection. On the COCO dataset, MLPNet implements the results of AP37.8.
Demodulation and Recognition of Image Transmission Signals for Small Uav Under Non-Cooperative Conditions
Authors: Zhou Zhiyong , Dong Zheng , Jia Tengfei , Dai Huanyao 
Published: February 2021

Abstract
According to the characteristics of CP-OFDM signals, analyzed and studied the subcarrier number estimation, timing synchronization and frequency offset compensation algorithms of CP-OFDM signals under non-cooperative reception conditions, and verified the feasibility of these algorithms based on UAV image transmission signals. As the results show, the algorithm proposed in the article can complete blind demodulation of the CP-OFDM signal and finally complete the sensing and recognition of small UAV signals under the condition of unknown CP-OFDM signal format by means of analysis and estimation algorithms.
Multisource Data-Driven Visual Analysis of Urban Crowd Travel
Authors: Zhang Jianzhong , Wang Fei , Guo Qian , Hong Anding , Chen Jiantao , Zhang Jin 
Published: February 2021

Abstract
Many functional areas have been formed in the development of the city, and the gathering of taxis can basically reflect the travel situation of the crowd. Combining urban functional areas and taxi GPS data to analyze crowd travel conditions can provide decision support for the construction of smart cities. Therefore, this paper analyzed the travel situation of crowd in Changsha urban area based on multi-source data. First, this paper clusters the distribution of public transit stations in Changsha, and then imports the geographic location information of the cluster centers into the Voronoi diagram in ArcGis to divide the urban area of Changsha. Then the divided areas and Point of interest (POI) data are counted and analyzed to obtain the functionality of each area. Finally, the taxi GPS data is clustered according to the time period, and the cluster center is mapped to the urban functional area, to analyzed the crowd clustering situation of each functional area in Changsha in different time periods.
Forecast of Gross Output Value of Agriculture and Forestry in Guangxi Based on Holt-Winters Model
Authors: Li Run , Li Xiangmin , Lu ZhiXiang 
Published: February 2021

Abstract
Agriculture and forestry are the source of food, clothing and subsistence for human beings and also the primary conditions for all production. The forecast of the gross output value of agriculture and forestry directly affects the purchasing, pricing and storage of important plans in the agricultural sector. This paper collects quarterly data from 2012 to 2020 in Guangxi Zhuang Autonomous Region and uses the Holt-Winters prediction model based on the multiplication to fit the historical data. The model was validated by calculating the optimal smoothing coefficient through the minimum residual sum of squares, making predictions and comparing actual values for the data from 2018 to 2020. Finally, the model was used to derive future forecasts for agriculture and forestry from 2020 to 2022, and recommendations for agricultural development were made based on the forecasts.
A Page-topic Relevance Algorithm Based on BM25 and Paragraph-Semantic Correlation
Authors: Peng Shi , Xie Xiaodan , Zhai Jia , Jia Yusheng , Gong Yuxuan 
Published: February 2021

Abstract
It is difficult for the traditional topic relevance algorithm based on word frequency and probability statistics model to deal with the ambiguous topic of search keywords, which leads to the retrieval results containing much information that users are not interested in. To solve this problem, a page-topic relevance algorithm based on BM25 and paragraph-semantic correlation is proposed in this paper. The semantic correlation of one page is calculated by the paragraph-semantic classification using a pre-trained deep neural network model, and is weighted with the BM25 retrieval score. Experimental results show that compared with the traditional BM25 algorithm, this algorithm can effectively improve the retrieval accuracy.
Research on Methods of Parsing and Classification of Internet Super Large-scale Texts
Authors: Song Mengting , Zheng Hang , Tao Zhen , Jiang Jia , Pan Bin 
Published: February 2021

Abstract
Web crawlers are an important part of modern search engines. With the development of the times, data has shown explosive growth, and mankind has entered a “big data era”. For example, Wikipedia, which carries knowledge achievements from all over the world, records real-time news that occurs every day and provides users with a good text search database[1]. Wikipedia updates data up to 50+GB every day. This project focuses on solving the problems of data acquisition and data analysis. At the same time, it downloads and parses the latest data of Wikipedia and analyzes XML files, and then uses SVM algorithm and Naive Bayes algorithm to classify articles, Train the model to download Wikipedia files efficiently and parse XML files.
Evaluation of Equipment Competitive Acquisition Management Based on Entropy Theory
Authors: Jiang Yujiao , Yuan Mingxing , Li Xiaosong 
Published: February 2021

Abstract
Aiming at the evaluation of equipment competitive acquisition management, the paper proposes an evaluation method based on entropy theory. First, the concept of equipment competitive acquisition management entropy is defined. Second, the conceptual model, mathematical model, and evaluation index system of equipment competitive acquisition management entropy are constructed. Finally, taking policy and regulatory entropy as an example, the paper has calculated and analyzed its evaluation results to prove the effectiveness of the method.
The Hybrid Intelligent Optimization Algorithm and Multi-objective Optimization Based on Big Data
Authors: Nie Xin , Luo Jian 
Published: February 2021

Abstract
Multi objective optimization problem (MOP) usually has more than two objective functions, and the optimal solution based on Pareto frontier is obtained. The traditional optimization algorithm cannot meet the needs of industrial application when dealing with multi-objective optimization problems. With the good performance of evolutionary algorithm in solving complex problems, its application field is also extended to multi-objective optimization problems. The Pareto optimal solution and evaluation system of multi-objective optimization problem are analysed. Particle swarm optimization (PSO), one of the evolutionary algorithms based on swarm intelligence, is briefly introduced. The combination of particle swarm optimization algorithm and multi-objective optimization is studied.
Research on E-commerce Supplier Selection Based on Big Data Analysis Technology
Authors: Guan Xin , Zhang Liyun , Zhao Haibo 
Published: February 2021

Abstract
In the analysis of e-commerce suppliers, the use of big data analysis technology is very important. The interference factors in the analysis process determine the supplier’s choice. According to the characteristics of e-commerce, the e-commerce competition model, evaluation matrix, and the intelligentized degree of each index are determined through level analysis. Experience has shown that accurate data on the financial status of the organization can be obtained through intelligent screening methods, which has laid a solid foundation for improving the good cooperation between the original electronic companies and suppliers.
Research on Information Security Defense Based on Improved Identity-Based Dynamic Clustering Authentication Algorithm
Authors: Guo Dandan , Zhang Ningchi , Wang Yanru , Cui Yanzhao , Jiang Bin , She Rui , Liu Hui , Ma Wenjie 
Published: February 2021

Abstract
Big data and artificial intelligence technology, as the unified means and carrier for collecting, storing and computing core data of contemporary power IoT, has diverse and complex characteristics of its data sources and types. And the lack of insecure data access, abnormal response and terminal access rights control leads to the failure of the integrity and credibility of the closed-loop information defense. In particular, wireless sensor networks (WSNs) in power scenarios are more susceptible to malicious attacks. To address the above problems, this paper proposes a power IOT information defense strategy based on improved identity-based dynamic clustering authentication algorithm (IIDC). First, the terminal device sets the private key to solve the key escrow problem of terminal security authentication in the IOT model. At the same time, the improved algorithm dynamically generates pseudo-cryptographic matrix to avoid collusion attack. Finally, a hierarchical privilege management mechanism is adopted to decrypt the terminal authentication once. The proposed algorithm is more suitable for terminal security access and power consumption requirements in WSN-based power IOT scenario, as verified by experimental simulation.
Research on the Construction of standardized WebGL 3D scene Service for Power Grid Service
Authors: Ji Kun , Wu Qiong , Zhang Yanfeng , Liu Ming , Zhen Chao , Zhu Jun , Dong XiangYu , Wang Taiping 
Published: February 2021

Abstract
With the increasing maturity of WebGL technology as well as the development and construction of the 3D geographic information system, 3D geographic information sharing service has become the trend of future development. There are numerous three-dimensional data sources of the power grid and many applications across departments and systems, but the three-dimensional geographic information service based on unified standards is still rarely seen. The paper studies and discusses the selection of 3D service standards and the implementation technology of 3D geographic information service for the power grid.
Multi-channel VideoStreaming Technology for Processing Based on Distributed Computing Platform
Authors: Cheng Wei , Tang Yanyang , Yang Wangdong 
Published: February 2021

Abstract
With the rapid development of the Internet, sensor network, and mobile Internet technology, a large number of data sets are continuously generated in the form of streaming in various application fields. At the same time, the processing of stream data has drawn more and more attention because of its application in different situations. To meet the urgent need for the processing of streaming data, there are many computing engines on the market, such as Spark and Flink. Traditional data-flow analysis has many problems in the process of streaming data, such as the high time delay, weak extensibility, and bad adaptability. In order to improve them, people use Kafka as the intermediate-cache and the computing engine that uses Spark Structured Streaming as the streaming data to realize the effective process of multichannel flow data. Considering the present demand for low latency and high flux that are analyzed by urban management in traffic video, processing the data of multichannel traffic video on distributed computing platform realized the tracking and search on motor vehicles, non-motor vehicles, and pedestrians of multiple roads and intersections.