bluuebunny/crossref_metadata_2025_split huggingface dataset:
The unequal-time matter power spectrum: impact on weak lensing observables
Authors: de la Bella Lucia F. , Tessore Nicolas , Bridle Sarah 
Published: August 2021

Abstract
We investigate the impact of a common approximation of weak lensing power spectra: the use of single-epoch matter power spectra in integrals over redshift.
We disentangle this from the closely connected Limber's approximation.
We derive the unequal-time matter power spectrum at one-loop in standard perturbation theory and effective field theory to deal with non-linear physics. We compare these formalisms and conclude that the unequal-time power spectrum using effective field theory breaks for larger scales. As an alternative we introduce the midpoint approximation.
We also provide, for the first time, a fitting function for the time evolution of the effective field theory counterterms based on the Quijote simulations.
Then we compute the angular power spectrum using a range of approaches: the Limber approximation, and the geometric and midpoint approximations. We compare our results with the exact calculation at all angular scales using the unequal-time power spectrum.
We use DES Y1 and LSST-like redshift distributions for our analysis. We find that the use of the Limber's approximation in weak lensing diverges from the exact calculation of the angular power spectrum on large-angle separations, ℓ < 10. Even though this deviation is of order 2% maximum for cosmic lensing, we find the biggest effect for galaxy clustering and galaxy-galaxy lensing. We show that not only is this true for upcoming galaxy surveys, but also for current data such as DES Y1.
Finally, we make our pipeline and analysis publicly available as a Python package called unequalpy.
Research on Vehicle Control System of Construction Machinery Based on Machine Vision
Authors: Huang Mian , Liu Dafeng 
Published: August 2021

Abstract
With the continuous development of construction machinery technology, great breakthroughs have been made in the application of electronic information technology. The control and operation system of construction machinery vehicles refers to the system that applies and receives certain instructions and makes the construction machinery produce specified actions according to the requirements of operators through its power transmission device. Compared with the previous construction machinery, the developing construction machinery has made good achievements in high efficiency, intensification, cost saving and environmental protection. The traditional speed measuring method can not meet the requirement of measuring accuracy of actual walking speed of construction machinery, and can not accurately reflect the actual walking speed and instantaneous motion state of construction machinery. This paper analyzes a kind of construction machinery vehicle using machine vision. It obtains important information through image processing, adopts intelligent control technology based on machine vision as an important control means of power drive, and can judge and control driving according to road conditions, thus improving the accuracy and stability of industrial autonomous transportation..
Youth physical training robot based on visual recognition and training method
Authors: Dong Hanbin , Cui Zhihe , Tian Liao 
Published: August 2021

AbstractWith the continuous improvement of people’s living standards, the continuous development of training methods and concepts, resulting in the continuous improvement of sports level, which puts higher demands on teenagers’ physical function. How to use high-efficiency and low-risk training methods to train has become a headache for coaches. In this paper, an accurate target detection and recognition method based on stereo matching algorithm is proposed. Based on stereo matching algorithm, after data training, the target can be accurately identified, and the position information of the target can be given. Then, the target can be accurately positioned by binocular vision and its three-dimensional coordinates can be obtained. Finally, it can be transmitted to the robot and control the robot to move and grab the target object. Experimental results show that this method can identify and locate the target accurately and quickly, and successfully guide the robot to grasp the target object. This physical training robot can provide theoretical basis and technical guidance for improving the physical training level and sports performance of young athletes.
Research on Key Technologies of Basketball Object Segmentation and Tracking Based on Image Analysis
Authors: Ma Liangying 
Published: August 2021

Abstract
Since the 21st century, with the continuous development of society, people’s living standards have been improved, and their physical qualities have been constantly improved. These factors have promoted the development rhythm of basketball, and at the same time put forward higher requirements for players’ abilities. In order to improve players’ basketball accomplishment quickly, many high-tech equipments are also used in basketball, which provides a lot of technical support for coaches to make training programs for players. With the rapid development of image processing technology, moving target detection technology in video has been more and more widely used. Tracking the flight trajectory in basketball shooting and analyzing the trajectory with image processing technology can improve the accuracy of basketball shooting. In this paper, a basketball tracking method based on image analysis is proposed, and an adaptive object model is further established. Then, the object model is used to improve the block matching method to track basketball. Finally, the tracking deviation is corrected, and the basketball tracking effectiveness detection mechanism is established.
Research on artificial intelligence machine learning character recognition method based on Feature Fusion
Authors: Qiao Xiaolin 
Published: August 2021

Abstract
In this paper, the artificial intelligence machine learning based on feature fusion is briefly introduced, and on this basis, the character recognition method based on artificial intelligence machine learning is studied from the perspective of character recognition system classifier and character recognition performance. The continuous development of artificial intelligence technology has made people more in-depth research on it. Machine learning has also become a new core research issue in artificial intelligence. It has been widely used in network search, language recognition, and machine vision. The emergence of artificial intelligence has brought great changes to people’s life and production mode, coupled with the development of science and technology and network technology, people have successfully entered the information age, and information technology is indispensable in all aspects of people’s life. With the progress of science and technology in the current era, artificial intelligence has developed very rapidly, and people’s research on machine learning has also made a breakthrough. Learning ability is an important intelligent behavior of human beings. Human beings can adjust their own actions through learning, so as to obtain strong environmental adaptability.
Research on Character Behavior Recognition Based on Local Spatio-temporal Relationship in Surveillance Video
Authors: Dong Weisong 
Published: August 2021

Abstract
Because of its wide application value, behavior recognition has long been one of the research hots pots in the field of computer vision and pattern recognition. At present, the method based on local features and word packet model has been widely used in the field of behavior recognition. However, this method does not consider the temporal and spatial relationship between features, and the local temporal and spatial relationship between features is very important for behavior representation and behavior recognition. In view of the above problems, this paper proposes a modeling method of character behavior recognition based on local spatio-temporal relationship in surveillance video. Firstly, each part of the proposed network model is introduced in detail, and then the proposed model is compared with the advanced skeleton action recognition methods in recent years on several skeleton data sets. Finally, the effectiveness of the proposed method is verified. The experimental results show that, compared with the recognition results of related literatures, the features extracted by choosing the starting point of trajectory have better recognition performance under the fusion framework.
Research on Face Recognition Technology Based on Improved YOLO Deep Convolution Neural Network
Authors: Fan Yu , Luo Yiyue , Chen Xianjun 
Published: August 2021

Abstract
With the development of information technology, deep learning has become a research hots pot in the field of computer vision, among which face recognition technology, as one of its applications, has received extensive attention in recent years. A face recognition method based on improved YOLOv3 deep convolution neural network is proposed. The feature pyramid network is used to obtain the four scale features of the target to fuse the shallow features and the deep features, and the influence weight of the loss function is adjusted according to the size of the detected target, so as to enhance the detection effect of small targets and mutually occluded objects, and the Softmax function is used for classification and recognition. Experimental results on mixed data sets show that this method can improve the real-time performance of face detection.
Research on Image Recognition Based on Neural Network
Authors: Zhang Jinhai 
Published: August 2021

Abstract
With the rapid development of Internet and mobile Internet, the image data in the network shows explosive growth. Image data is simple and intuitive, and contains rich information, which is widely used as a carrier of information exchange. Content-based image recognition can start from the image itself, first extract significant features from the image, and then recognize the image according to the differences of features, with good recognition effect. Neural network plays a very important role in artificial intelligence. Neural network is the key to realize artificial intelligence by learning data through algorithms and then making predictions and decisions. The image recognition of neural network is based on the image content. Neural network starts from the image data itself, extracts the image features of the bottom layer from the data, and then establishes the relationship between the image semantics of the bottom layer and the top layer through the algorithm of neural network, thus realizing the image recognition.
Research on Ship Collision Avoidance Expert System in big data Era
Authors: Tong Jinqiang 
Published: August 2021

Abstract
In the era of big data, in order to effectively reduce or avoid the occurrence of ship collision accidents caused by human errors, adapt to the development trend of ship Manning reduction, promote the research of ship automation, and ensure the safety of ship navigation at sea, based on the in-depth study of the principle of ship collision avoidance, this paper uses expert system technology and a large number of quantitative research results of ship collision avoidance, A more reasonable and complete knowledge base of collision avoidance is established, and a collision avoidance expert system is designed and partially implemented, which can help the ship’s pilot to make decision of collision avoidance in real time and realize automatic collision avoidance under certain conditions.
Research and Application of Computer Big Data System in Early-Warning Methods and Online Monitoring Technologies
Authors: Su Hongyu , Wang Juan 
Published: August 2021

Abstract
Corporate financial index is a collection of corporate financial information, which can truly and reliably reflect the financial status of a company. In order to monitor the size of corporate financial risks and prevent and reduce risks from bringing crises to enterprises, this paper proposes a financial early-warning computer big data model for listed real estate companies based on extension theory based on traditional financial early-warning methods, and combining them with each company The specific actual data is verified to provide suggestions for the development of listed real estate companies quickly and dynamically.
Application of Wireless Sensor Networks in the Sensitive Data Security of Intelligent Data Center under the Big Data Environment
Authors: Lin Han , Weng Binxin , Pan Jianli , Lin Chaodeng , Yang Qifan 
Published: August 2021

Abstract
In the big data environment, wireless sensor networks (WSN) are constantly improving themselves and are widely used in all walks of life. The WSN collects, processes and transmits the information of the sensed object in the monitoring area. In recent years, WSN have also made good progress in environmental change detection, traffic control, and sensitive data security management. The purpose of this article is to study the application of WSN in the security management of sensitive data in the data center under the big data environment. This article first describes the characteristics of WSN, and discusses the security management requirements of sensitive data in WSN based on big data technology. Secondly, it analyzes the problems in the security management of sensitive data and conducts research from different directions. Finally, in response to these problems, the implementation of the WSN in the security management of sensitive data provides some help to solve the problem. Thereby effectively reducing the energy consumption of network nodes, reducing the amount of data transmitted by the network, and extending the life cycle of the WSN. Experimental research results show that the most demanded level in security management is data confidentiality, which is 47.15%, followed by data freshness and data integrity, which are 28.49% and 24.36%, respectively. In summary, it is necessary to increase efforts to improve the security management of sensitive data.
Application of Clustering Algorithm by Data Mining in the Analysis of Smart Grid from the Perspective of Electric Power
Authors: Qi Yaru , Ren Junda , Sun Ni , Yu Yangqi 
Published: August 2021

Abstract
In recent years, the research on the relationship between economic development and power consumption is also a focus of government departments at all levels. The development of electric power (EP) not only has an impact on power supply enterprises and EP industry, but also is closely related to the social and economic development of the whole region and residents’ life, that is, there must be some internal relationship between economic development and EP consumption. In many areas of our country, the situation of power consumption and economic development is not coordinated. Insufficient and untimely power supply will hinder economic growth, and excessive power supply will bring unnecessary waste of resources. This also reflects that the research on the relationship between economic development and power consumption is not in-depth, so the research on the relationship between power consumption and economic development has a certain theoretical significance. This paper studies the application of clustering algorithm based on data mining in the analysis of economic development characteristics(EDC) from the perspective of EP, uses K-means clustering algorithm to understand the relationship between EP development and economic development, understands its characteristic development application, studies the relationship between EP consumption and economy from the EP elastic coefficient method and output value unit consumption method, and uses K-means clustering algorithm to calculate, this paper uses the chart analysis method to analyze the correlation degree of power consumption structure and the relationship between different industrial GDP and power consumption. The results show that the total output of each industrial structure is in direct proportion to the power consumption. The total value of each industrial structure is increasing, and the power consumption is on the rise. For example, in 2016, the total value of the first industry was 186.81 trillion yuan, the second industry was 960.54 trillion yuan, the third industry was 515.96 trillion yuan, and the power consumption was 41.246 billion kwh, by 2020, the total value of the primary industry will be 220.66 trillion yuan, the secondary industry 1916.51 trillion yuan, the tertiary industry 866.22 trillion yuan, and its electricity consumption (EC) will be 57.697 billion kwh.
Data Mining and Intelligent Matching Algorithm in Electric Power Terminology Database
Authors: Chen Yanan , Lin Yuxi , Fang Luebin , Cheng Xiuyuan , Wang Weilan 
Published: August 2021

Abstract
The paper designs a method and system for automatic extraction of professional vocabulary in the electric power field based on data mining and matching algorithms. The system extracts professional vocabulary related to power marketing from the corpus in the field of power marketing, converts it into corresponding English and adds it to the professional vocabulary of power marketing. Then we will collect the relevant vocabulary of the power system and the corresponding translation and add it to the professional vocabulary of power marketing. The thesis establishes a two-level index structure for the professional vocabulary of electric power marketing. We search the professional vocabulary database of electric power marketing, the order of retrieval is electric power system vocabulary, general dictionary, electric power marketing field extraction vocabulary, and the search result is returned to the user after the retrieval. The experiment verified that the system facilitates the efficient retrieval of user vocabulary, and then realizes the automatic conversion of professional vocabulary in the marketing standardization design results, so as to improve the quality and efficiency of professional vocabulary conversion.
Research on Intelligent Parking Space Design in Limited Space through Big Data
Authors: Zou Han , Zhu Tianjian 
Published: August 2021

Abstract
With the rapid development of urbanization and motorization in China, the problem of parking in the city becomes more and more serious. Due to a series of reasons such as spatial layout, insufficient parking facilities and lagging property management, the parking problem in old urban communities is difficult to be solved. Based on the perspective of big data, this paper analyzes the reasons for the parking difficulties in the old community, and designs a set of intelligent parking system to provide an effective solution to the problem of parking difficulties in the old community. At the same time, it proposes design strategies for the renewal of parking space in the old community.
The impact of AR-HUD intelligent driving on the allocation of cognitive resources under the breakthrough of 5G technology
Authors: Ma Xiangdong , Hong Zhicong , Huang Junhong , He Jingjing , Song Jianpeng , Jia Mengting 
Published: August 2021

Abstract
This paper focuses on the establishment of an ARHUD assisted driving test system based on a VR platform, which has the advantages of high security and immersion, repeatable experiments and the ability to perform eye-movement analysis. This paper first defines and designs the vehicle driving safety icons based on human-computer interaction principles and engineering psychology, supplemented by PS to define and design the AR-HUD interface while combining mental load and other factors, then uses 3Dsmax software to build the 3D model material required for driving, then builds the driving environment and designs various driving emergencies in Unity based on featured technologies such as multi-channel rendering and global illumination, and then combines HTC VIVE Pro eye display. Thirty drivers were then tested on a distraction task. Analysis of the subjects’ eye-movement data revealed that the AR-HUD system improved the cognitive efficiency of the drivers compared to the traditional driving method while allocating cognitive resources to the central driving area, speed module, navigation information, and hazard warnings in a balanced manner, thus improving the ability to react to unexpected driving events.
Research on Modelling of Innovation Information System in Smart Libraries Using Computer Big Data
Authors: Zhou Yufeng 
Published: August 2021

Abstract
To change the traditional ideological and political teaching in higher vocational colleges, students passively accept knowledge, and the teaching efficiency is low. The article developed an ideological and political network teaching system based on computer big data NET. The teaching system uses NET computer platform technology and castle framework and adopts the MVC system architecture model based on the B/s three-tier structure to develop the teaching system. It was found that the ideological and political network teaching system is running stably during several tests and use by the developers. The system page is displayed normally, and all functions of the system can be correctly responded. The system meets the design requirements.
LSTM Framework Design and Volatility Research on Intelligent Forecasting Model for Solving the Parallel Dislocation Problem
Authors: Weng Yiran , Wang Zhiyi , Zhou Longzhen 
Published: August 2021

Abstract
The yield of treasury bonds is the benchmark interest rate in the financial market which is worth predicting and judging. Based on the Long Short-Term Memory (LSTM) neural network model in deep learning, combined with the vector autoregression method (VAR), this paper creatively constructs the VAR-LSTM framework and uses the predicted values of macroeconomic variables and lagged value of the time sequence as input factors to solve the problem of “parallel dislocation” of the fitting results of the traditional LSTM model which significantly improves the prediction accuracy. In order to meet the requirements of active quantitative investment for high precision prediction of stock market index, adaptive noise complete ensemble empirical mode decomposition (EMD) is introduced into the modeling of stock market index prediction. Combined with the efficient modeling ability of long-term and short-term memory network for medium- and long-term dependence of complex series, using the idea of “decomposition-reorganization-prediction-integration”, an integrated prediction method of stock market index CEEMDAN-LSTM is proposed. CEEMDAN is used to decompose and reconstruct the index to obtain its high and low frequency components and trend items. The LSTM prediction models of each component are constructed respectively and the IMF reorganization mode of high frequency subseries is optimized. Then the overall predicted value of the index is obtained by adding and integrating the predicted values of each component. Taking five representative stock market indexes as test data, the prediction results of CEEMDAN-LSTM and mainstream financial time series machine learning modeling methods are compared systematically. The results show that for treasury bond yield series, the prediction accuracy of ARIMA model is higher than that of general LSTM method, while VAR-LSTM model is better than ARIMA model. The prediction error in the training set and the test set is reduced by about 55% and 50% respectively, and the prediction accuracy of the change direction is improved by about 5% and 8% respectively, which has higher application value. The prediction performance of CEEMDAN-LSTM is consistently better than that of existing modeling methods, and has less prediction error and lower lag.