bluuebunny/crossref_metadata_2025 huggingface dataset:
How to enhance quantum generative adversarial learning of noisy information
Authors: Braccia Paolo , Caruso Filippo , Banchi Leonardo
Published: April 2021

Quantum machine learning is where nowadays machine learning (ML) meets quantum information science. In order to implement this new paradigm for novel quantum technologies, we still need a much deeper understanding of its underlying mechanisms, before proposing new algorithms to feasibly address real problems. In this context, quantum generative adversarial learning is a promising strategy to use quantum devices for quantum estimation or generative ML tasks. However, the convergence behaviours of its training process, which is crucial for its practical implementation on quantum processors, have not been investigated in detail yet. Indeed here we show how different training problems may occur during the optimization process, such as the emergence of limit cycles. The latter may remarkably extend the convergence time in the scenario of mixed quantum states playing a crucial role in the already available noisy intermediate scale quantum devices. Then, we propose new strategies to achieve a faster convergence in any operating regime. Our results pave the way for new experimental demonstrations of such hybrid classical-quantum protocols allowing to evaluate the potential advantages over their classical counterparts.
Entanglement distribution with wavevector-multiplexed quantum memory
Authors: Lipka Michał , Mazelanik Mateusz , Parniak Michał
Published: April 2021

Feasible distribution of quantum entanglement over long distances remains a fundamental step towards quantum secure communication and quantum network implementations. Quantum repeater nodes based on quantum memories promise to overcome exponential signal decay inherent to optical implementations of quantum communication. While performance of current quantum memories hinders their practical application, multimode solutions with multiplexing can offer tremendous increase in entanglement distribution rates. We propose to use a wavevector-multiplexed atomic quantum memory (WV-MUX-QM) as a fundamental block of a multiplexed quantum repeater architecture. We show the WV-MUX-QM platform to provide quasi-deterministic entanglement generation over extended distances, mitigating the fundamental issue of optical loss even with currently available quantum memory devices, and exceeding performance of repeaterless solutions as well as other repeater-based protocols such as temporal multiplexing. We establish the entangled-bit (ebit) rate per number of employed nodes as a practical figure of merit reflecting the cost-efficiency of larger inter-node distances.
Mathematical modeling of chemotaxis guided amoeboid cell swimming
Authors: Wang Qixuan , Wu Hao
Published: April 2021

Cells and microorganisms adopt various strategies to migrate in response to different environmental stimuli. To date, many modeling research has focused on the crawling-based Dictyostelium discoideum (Dd) cells migration induced by chemotaxis, yet recent experimental results reveal that even without adhesion or contact to a substrate, Dd cells can still swim to follow chemoattractant signals. In this paper, we develop a modeling framework to investigate the chemotaxis induced amoeboid cell swimming dynamics. A minimal swimming system consists of one deformable Dd amoeboid cell and a dilute suspension of bacteria, and the bacteria produce chemoattractant signals that attract the Dd cell. We use the mathematical amoeba model to generate Dd cell deformation and solve the resulting low Reynolds number flows, and use a moving mesh based finite volume method to solve the reaction–diffusion–convection equation. Using the computational model, we show that chemotaxis guides a swimming Dd cell to follow and catch bacteria, while on the other hand, bacterial rheotaxis may help the bacteria to escape from the predator Dd cell.
Taking snapshots of a moving electron wave packet in molecules using photoelectron holography in strong-field tunneling ionization*
Authors: He Mingrui , Fan Yang , Zhou Yueming , Lu Peixiang
Published: April 2021

Coherent superposition of electronic states induces attosecond electron motion in molecules. We theoretically investigate the strong-field ionization of this superposition state by numerically solving the time-dependent Schrödinger equation. In the obtained photoelectron momentum distribution, an intriguing bifurcation structure appears in the strong-field holographic interference pattern. We demonstrate that this bifurcation structure directly provides complete information about the status of the transient wave function of the superposition state: the horizontal location of the bifurcation in the momentum distribution reveals the relative phase of the involved components of the superposition state and the vertical position indicates the relative coefficient. Thus, this bifurcation structure takes a snapshot of the transient electron wave packet of the superposition state and provides an intuitive way to monitor electron motion in molecules.
Elastic electron scattering with formamide-(H2O)
n
complexes (n = 1, 2): Influence of microsolvation on the π
* and σ
* resonances*
Authors: Wang Kedong , Wang Yan , Liu Jie , Wang Yiwen , Zhang Haoxing
Published: April 2021

We report elastic cross sections for low-energy electron scattering with formamide-(H2O)
n
complexes (n = 1, 2) in the energy region of 0.01–8 eV. The scattering calculations are performed using the R-matrix method in the static-exchange (SE) approximation. We consider three structures of formamide–H2O and six structures of formamide–(H2O)2 in the present work. Our purpose is to investigate effects of water molecules hydrogen-bonding to formamide. We focus on the influence of microsolvation on the π
* and σ
* resonances of formamide. The scattering result for complexes shows that the position of π
* resonance appears at lower or higher energies in the cluster than in the isolated formamide depending on the complex structure and the water role in the hydrogen bonding. We explain this behavior according to the net charge of the solute. It is found that the microsolvation environment has a substantial effect on the width of π
* resonance. Our results indicate that surrounding water molecules may affect the lifetime of the resonances, and hence the process is driven by the anion state, such as the dissociative electron attachment.
Synthesis of ternary compound in H–S–Se system at high pressures*
Authors: Zhang Xiao
Published: April 2021

The chemical reaction products of elemental sulfur (S), selenium (Se), and molecular hydrogen (H2) at high pressures and room temperature are probed by Raman spectroscopy. Two known compounds H2S and H2Se can be synthesized after laser heating at pressures lower than 1 GPa. Under further compression at room temperature, an H2S–H2Se and an H2S–H2Se–H2 van der Waals compounds are synthesized at 4 GPa and 6 GPa, respectively. The later is of guest–host structure and can be identified as (H2S)
x
(H2Se)(2−x)H2. It can be maintained up to 37 GPa at least, and the stability of its H2Se molecules is extended: the H–Se stretching mode can be detected at least to 36 GPa but disappears at 22 GPa in (H2Se)2H2. The pressure dependence of S–H and Se–H stretching modes of this ternary compound is in line with that of (H2S)2H2 and (H2Se)2H2, respectively. However, its hydrogen subsystem only shows the relevance to (H2S)2H2, indicating that this ternary compound can be viewed as H2Se-replaced partial H2S of (H2S)2H2.
Investigation of hypersonic flows through a cavity with sweepback angle in near space using the DSMC method*
Authors: Guo Guangming , Chen Hao , Zhu Lin , Bian Yixiang
Published: April 2021

Near space has been paid more and more attentionin recent years due to its military application value. However, flow characteristics of some fundamental configurations (e.g., the cavity) in near space have rarely been investigated due to rarefied gas effects, which make the numerical simulation methods based on continuous flow hypothesis lose validity. In this work, the direct simulation Monte Carlo (DSMC), one of the most successful particle simulation methods in treating rarefied gas dynamics, is employed to explore flow characteristics of a hypersonic cavity with sweepback angle in near space by considering a variety of cases, such as the cavity at a wide range of altitudes 20–60 km, the cavity at freestream Mach numbers of 6–20, and the cavity with a sweepback angle of 30°–90°. By analyzing the simulation results, flow characteristics are obtained and meanwhile some interesting phenomena are also found. The primary recirculation region, which occupies the most area of the cavity, causes pressure and temperature stratification due to rotational motion of fluid inside it, whereas the pressure and temperature in the secondary recirculation region, which is a small vortex and locates at the lower left corner of the cavity, change slightly due to low-speed movement of fluid inside it. With the increase of altitude, both the primary and secondary recirculation regions contract greatly and it causes them to separate. A notable finding is that rotation direction of the secondary recirculation region would be reversed at a higher altitude. The overall effect of increasing the Mach number is that the velocity, pressure, and temperature within the cavity increase uniformly. The maximum pressure nearby the trailing edge of the cavity decreases rapidly as the sweepback angle increases, whereas the influence of sweepback angle on velocity distribution and maximum temperature within the cavity is slight.
Automatic classification of idiopathic Parkinson’s disease and atypical Parkinsonian syndromes combining [11C]raclopride PET uptake and MRI grey matter morphometry
Authors: Martins Ricardo , Oliveira Francisco , Moreira Fradique , Moreira Ana Paula , Abrunhosa Antero , Januário Cristina , Castelo-Branco Miguel
Published: April 2021

Objective.To explore the viability of developing a computer-aided diagnostic system for Parkinsonian syndromes using dynamic [11C]raclopride positron emission tomography (PET) and T1-weighted magnetic resonance imaging (MRI) data.Approach.The biological heterogeneity of Parkinsonian syndromes renders their statistical classification a challenge. The unique combination of structural and molecular imaging data allowed different classifier designs to be tested. Datasets from dynamic [11C]raclopride PET and T1-weighted MRI scans were acquired from six groups of participants. There were healthy controls (CTRLn= 15), patients with Parkinson’s disease (PDn= 27), multiple system atrophy (MSAn= 8), corticobasal degeneration (CBDn= 6), and dementia with Lewy bodies (DLBn= 5). MSA, CBD, and DLB patients were classified into one category designated as atypical Parkinsonism (AP). The distribution volume ratio (DVR) kinetic parameters obtained from the PET data were used to quantify the reversible tracer binding to D2/D3 receptors in the subcortical regions of interest (ROI). The grey matter (GM) volumes obtained from the MRI data were used to quantify GM atrophy across cortical, subcortical, and cerebellar ROI.Results.The classifiers CTRL vs PD and CTRL vs AP achieved the highest balanced accuracy combining DVR and GM (DVR-GM) features (96.7%, 92.1%, respectively), followed by the classifiers designed with DVR features (93.3%, 88.8%, respectively), and GM features (69.6%, 86.1%, respectively). In contrast, the classifier PD vs AP showed the highest balanced accuracy (78.9%) using DVR features only. The integration of DVR-GM (77.9%) and GM features (72.7%) produced inferior performances. The classifier CTRL vs PD vs AP showed high weighted balanced accuracy when DVR (80.5%) or DVR-GM features (79.9%) were integrated. GM features revealed poorer performance (59.5%).Significance.This work was unique in its combination of structural and molecular imaging features in binary and triple category classifications. We were able to demonstrate improved binary classification of healthy/diseased status (concerning both PD and AP) and equate performance to DVR features in multiclass classifications.
An automatic sleep disorder detection based on EEG cross-frequency coupling and random forest model
Authors: Dimitriadis Stavros I , Salis Christos I , Liparas Dimitris
Published: April 2021

Objective. Sleep disorders are medical disorders of a subject’s sleep architecture and based on their severity, they can interfere with mental, emotional and physical functioning. The most common ones are insomnia, narcolepsy, sleep apnea, bruxism, etc. There is an increased risk of developing sleep disorders in elderly like insomnia, periodic leg movements, rapid eye movement behavior disorders, sleep disorder breathing, etc. Consequently, their accurate diagnosis and classification are important steps towards an early stage treatment that could save the life of a patient. Approach. The electroencephalographic (EEG) signal is the most sensitive and important biosignal, which is able to capture the brain sleep activity that is sensitive to sleep. In this study, we attempt to analyze EEG sleep activity via complementary cross-frequency coupling (CFC) estimates, which further feed a classifier, aiming to discriminate sleep disorders. We adopted an open EEG database with recordings that were grouped into seven sleep disorders and a healthy control. The EEG brain activity from common sensors has been analyzed with two basic types of CFC. Main results. Finally, a random forest (RF) classification model was built on CFC patterns, which were extracted from non-cyclic alternating pattern epochs. Our RFCFC model achieved a 74% multiclass accuracy. Both types of CFC, phase-to-amplitude and amplitude–amplitude coupling patterns contribute to the accuracy of the RF model, thus supporting their complementary information. Significance. CFC patterns, in conjunction with the RF classifier proved a valuable biomarker for the classification of sleep disorders.
Testing and Evaluation of a 20-Gsps High-Resolution Waveform Digitizer Based on TIADC Technique
Authors: Dong Ruoshi , Zhao Lei , Qin Jiajun , Zhong Wentao , Fan Yichun , Liu Shubin , An Qi
Published: April 2021

Time-Interleaved Analog-to-Digital Conversion (TIADC) is one of the significant technique in high speed waveform digitization systems. In our latest work, a 20-Gsps 12-bit TIADC system is designed and a series of tests are conducted on the proposed system and a broadband mismatch error correction is implemented. The test methods and results are specifically presented in this paper. The test results show that the -3dB bandwidth of the proposed system is 6 GHz and the ENOB performance is about 8.7 bits at 647 MHz and 7.6 bits at 6 GHz which is significantly enhanced by the mismatch error correction process.
Research on Quantum Programming Framework Based on NISQ Function
Authors: Liu Xiaonan , Xu Le , Hu Chunchao , Jin Lei
Published: April 2021

In the past two decades, the field of quantum computing has developed rapidly, is gradually able to solve some difficult classical problems. With the development of the facilities for Noisy Intermediate-Scale Quantum(NISQ), the NISQ technology addresses the existing Quantum programming languages or frameworks, suggesting that they cannot fully meet the requirements of Quantum computers. In recent years, some quantum programming languages or frameworks have emerged which claim to support the classical computing algorithms of heterogeneous quantum. However, the code is too cumbersome and redundant, and it is difficult to map the code to heterogeneous quantum classical computation while meeting the requirements of timing. Therefore, a modular programming framework of heterogeneous quantum classical computing algorithm with NISQ function is proposed. The framework optimizes the quantum code and retains the quantum classical interactions that satisfy the temporal constraints.
Discussion on Intelligent Disposal System of High-fat Food Waste
Authors: Lu Duan , Zhou Zhou
Published: April 2021

Food waste contains high organic matter and high fat. Discomfort in handling will cause environmental pollution and ultimately affect people’s lives. For a diet characteristic of high starch and high fat in the northwest region, a system that can effectively dispose of kitchen waste is designed. The kitchen waste first needs to pass through the filter of the pre-treatment device and crush it to obtain proper disposal and become particle size. The control system is transferred to the solid-liquid separation device, and the separated liquid is transported to the oil-water separation device through the liquid delivery pipe. The oil-water liquid can be heated by a clean energy heating box, and the PLC control system is used to complete the final oil-water separation. The food waste disposal system designed in this paper can efficiently separate the oil and water, thereby quickly removing the high starch and high oil components in the food waste. And make full use of a series of green and clean energy such as solar energy to keep running, relatively high degree of automation, simple operation and maintenance, and powerful practicability.
RETRACTED: Research on Optimization Plan of Enterprise Employee Training under the Background of Big Data
Authors: Wang Yafen , Bai Xincheng
Published: April 2021

This article takes SMEs as the research object, and aims to study the problems and optimization schemes of SME employee training under the background of big data. This article uses a combination of literature reading, statistical analysis and questionnaire survey to investigate the employees of a certain company’s specific production base, and conducts research and analysis on the company’s employee training management. This article first introduces some concepts of modern enterprise training, and then analyzes the status quo of enterprise employee training. Through the interviews and questionnaires issued to the employees of the company, it analyzes the problems and causes of the company’s employee training management, and provides optimized solutions for the company’s employee training. The experimental results of this paper show that the optimization of corporate employee training programs under the background of big data plays a positive role in corporate management. It improves employee enthusiasm and work commitment, and increases the number of corporate trainings.
Big data and industrial innovation progress in Jiangxi Province incremental effect highlights enabling digital economy cultivation
Authors: Cheng Caijun , Huang Huazhen
Published: April 2021

With the advent of the Internet information age, most social and economic activities rely on data. As a new resource, big data technology plays an important role in the social digital economy. Therefore, it is not only the objective requirement of economic development, but also the value of big data resources to promote the close integration of big data and industry, and help the upgrading and development of industry with big data. In order to emphasize the strength of digital economy, this paper studies the gradual effect of big data and industrial innovation in Jiangxi Province, and carries out a series of studies. With the development of the times, the most popular Internet also changes with the development of the times. The Internet has changed from the IT era at the beginning to the new digital technology era. The creation of big data technology and the establishment of database have become the storage and source direction of new resources for all industries. At present, the digital economy is facing the opportunities and challenges of the “Internet plus” new market. Digital economy related enterprises should strengthen mutual cooperation among resources supply network organizations such as information and information, dissolve and reorganize production factors, strengthen the competitiveness among industrial economies, promote the optimization and upgrading of industrial economic structure, and expand the market space for which the industry can develop. However, the development of the Internet plus industry depends on the innovative investment and innovation power of the industry. In the era of big data, one of the ways to improve the overall level of the industry is innovation. This paper discusses how much influence big data shows in the cultivation of digital economy.
Evaluation System of College English Teaching Based on Big Data
Authors: Tan Qingyan
Published: April 2021

Teaching quality is the fundamental guarantee for colleges and universities(CAU) to achieve their educational goals, and is the basis for training talents. In recent years, an important research field of big data is the research of Data mining(DM). The use of DM algorithms can find out the key factors affecting teaching quality, provide a strong basis for teaching arrangements and teaching management in universities, and then improve the teaching level of universities. The purpose of this article is to study the evaluation system of college English teaching(ET) based on big data. Based on the idea of DM, this paper designs and implements a college ET quality evaluation system(ETQES), and applies the system to the college ET quality monitoring system, and obtains a large amount of evaluation data. This paper extracts relevant data from the school’s ETQES, teacher management system, comprehensive educational administration and other system databases, and builds a data warehouse through data preprocessing. This paper implements the DM module of the ETQES to mine the data in the data warehouse. Through the analysis of these rules, the factors affecting the evaluation results of ET quality are obtained. This paper establishes a database suitable for mining association rules. Through the analysis of association rules, find out which key factors can affect the quality of ET, so as to provide a strong basis for ET decision-making and management. According to the experimental results, it can be concluded that the 4555-year-old teacher gets a higher student evaluation score, indicating that teachers of this age group are more popular with students.
Construction Project Cost Management Model Based on Big Data
Authors: Xu Jing
Published: April 2021

The advent of the era of big data and the development of machine learning have brought new opportunities to the field of engineering cost in China. This paper mainly studies the construction project cost management model based on big data. On the analysis of the listing of large amount of data that exist in the proper nouns, the characteristics of dispersion characteristics of distribution, based on these features to implement and compare several listing classification method, the effect of and analyzes the reason of the result, refer to the classification results of traditional method at the same time, puts forward a project cost listing standardization based on polynomial bayesian classification method. In this paper, we propose a method to detect abnormal data by using the comprehensive unit price as the classification label, and study its association with the description of list according to the list classification method, and verify the effectiveness of this method through experiments.
RETRACTED: Sports Teaching Practice Innovation Mode Based on Big Data Technology
Authors: Zheng Li
Published: April 2021

The arrival of the era of big data provides a new vision and broad thinking for college sports education. this article mainly studies the innovation mode of sports education practice based on big data technology. Through questionnaire survey, interview, comparative analysis, field survey, mathematical statistics, this article aims to explore the purpose, means, measures and ways of application of big data in public sports education, test the effectiveness and provide suggestions develop public sports education course, enrich the theoretical research system of public sports, broaden the theoretical research scope of big data application and promote big data The development of interdisciplinary theory with public sports education.
International Competitiveness of Jilin Province Automobile Industry Based on Big Data and Low-Carbon Economy
Authors: Zhao Xianmin
Published: April 2021

With the rapid development of economic globalization at this stage, the ecological environment of the earth has become one of the hot issues of global concern. As the largest developing country in the world, our country faces the highest carbon emissions and increase in the automotive industry. From the perspective of the integration of big data and low-carbon economy, the international competitiveness of the automobile industry in Jilin Province has also been continuously improved. Based on the situation in Jilin Province, this paper constructs a comprehensive carbon emission research framework suitable for the low-carbon economic automobile industry in Jilin Province. Through this framework, the automobile industry of our country’s Jilin Province can be improved internationally from the perspective of low-carbon economy. This article first analyzes and elaborates the theory of low-carbon concept, and through the theoretical research foundation of low-carbon economy, studies the theoretical foundation of Jilin Province’s automobile industry’s transition to low-carbon economy from the perspective of big data. Secondly, it analyzes the status quo of Jilin Province. Through the use of the extended model of kaya identity and the LMDI logarithmic decomposition method, based on the carbon emissions data of the automobile industry in Jilin Province from 2015 to 2020, the quantitative analysis of energy intensity and economic growth. The impact of five factors, including population, automobile industry structure and energy structure, on carbon emissions in Jilin Province from the perspective of low-carbon economy, will be studied in depth. Finally, the peak value of Tai emission in Jilin Province is calculated, the IPAT model is constructed using Eviews software, and the prediction formula of carbon emission is obtained. The value of carbon emission in Jilin Province is simulated by Matlab software and compared with foreign countries. The experimental research results show that the use of big data analysis technology for technological development in the automotive industry to achieve low-carbon economic development has greatly promoted the competitiveness of Jilin Province’s automotive industry in the international industry.
Big Data Security Storage Based on Hybrid Large-scale Database
Authors: Peng Xuemei , Hu Jie
Published: April 2021

With the development and progress of network technology, hidden dangers of network security have also become a threat to network storage systems. This research mainly discusses the secure storage of big data based on hybrid large-scale databases. The user sends an authentication request to the SSP, and the SSP verifies the user’s time stamp ts to ensure that it is not a replayed message, and checks whether the user ID is legal. The virtualization layer logically divides the hardware devices at the facility layer through virtualization technology, provides unified services to the outside world, and supports the operation of the platform layer; the platform layer builds a file system and database to provide an operating environment for the operation layer; the operation layer provides users with a big data environment resource access behavior and platform traffic are reasonably allocated and managed, platform business deployment, etc.; the application layer provides users with big data computing services. During the file download process, the big data storage module is relatively fast (300-600k/s), while the speed of Seccloud and invisible cloud is relatively slow (200-400k/s). This research helps to improve the data access efficiency of the big data secure storage module.
Information Teaching Platform of College Physical Education Based on Artificial Intelligence Technology
Authors: Li Feifei
Published: April 2021

These years, with the continuous in-depth reform of domestic education, the educational model of high school physical education is changing. The teaching system of physical education curriculum based on AI technology can not only improve the working efficiency of management departments, but also promote the construction of digital, information and intelligent campus. The main purpose of this study is to analyze the basic characteristics of artificial intelligence and the significance and impact of teaching reform.This paper discusses the new idea of artificial intelligence in the elements of teaching activities by using interview method and case analysis method, which enlightens the application of artificial intelligence teaching and promotes the intelligent development of education and teaching.Based on the universities and AI technology as the research direction, this paper puts forward the introduction of AI into physical education curriculum education, and realizes individualized targeted teaching by constructing Agent layer and data service layer. By comparing the results of the control group and the experimental group, and through the investigation of the students in the experimental group, the system has improved the students’ physical education achievement to a certain extent. Compared with the control group, the experimental group improved the students’ interest in sports.
RETRACTED: Design of College English Teaching Information Platform Based on Artificial Intelligence Technology
Authors: Yuan Xinhua
Published: April 2021

With the development of Artificial Intelligence(AI) technology, especially in the construction of College English teaching(ET) IP(IP), more and more applications have promoted the current college ET reform and development. We first analyzes the connotation of AI technology and the related technical problems that need to be solved in the design of ET IP, then analyzes its technology in the development of English learning through the discussion of AI technology, and finally analyzes the application of AI technology from many different aspects. Listening, writing, translation and oral ET.Secondly, under the guidance of modern education theory and learning theory, combined with the information environment, combined with teaching practice, using AI technology and information technology to design college students’ English IP. Through the theoretical construction of the traditional ET model, through interviews and questionnaire surveys, some problems that exist in the learning of the English IP are found. AI technology and new media in information technology are applied to the actual teaching practice of college English, and the whole teaching practice process is tracked and analyzed. In the process of tracking and analysis, the teaching model is continuously improved. The analysis of the teaching practice results shows that “under the mode of College English education informatization, the construction of College English based on AI technology” improves students’ learning ability and greatly improves their English learning achievements. The experimental results show that in the current network connected information society, the construction of College English education IP should abide by the traditional education concept, completely innovate the education mode, and provide the greatest advantages of AI technology and information technology.
Mechanical Automation Design and Manufacturing Based on Artificial Intelligence Technology
Authors: Han Tianrong , Xu Yeqin
Published: April 2021

The development of mechanical design and manufacturing and automation engineering is not only an effective support to realize the goal of China’s industrial power and improve the gross national product, but also can improve the people’s living standards and provide the production basis for the current social development. The development of artificial intelligence can promote the degree of mechanical automation. In order to better distinguish the influence of artificial intelligence technology on mechanical design and manufacturing, we selected 30 students from mechanical college and equally divided them into two groups. Group A students used traditional mechanical design and manufacturing method to carry out experiments, while group B used artificial intelligence on this basis be able to design and manufacture machinery. With the development of experiment time and progress, we found that there was a big difference between the two groups of students. The experimental results show that when the mechanical manufacturing reaches 30%, the waste rate of group A is 0.4%, and the waste rate of group B is 0.2%, saving 0.2%. When the manufacturing progress reaches 50% and 70%, the waste of resources in group B is 0.3% less than that in group A 5% in group A and 1% in group B, saving 0.5%. Thus, the use of artificial intelligence technology can not only improve the manufacturing efficiency of mechanical manufacturers, but also reduce the waste of resources, which has a profound impact on the rapid development of mechanical design and manufacturing.
Data of E-Commerce Users Based on Data Mining Technology
Authors: Qing Haohua , Zhang Jiali , Fu Die
Published: April 2021

With the rapid development of information technology, the rapid development of e-commerce Internet has been involved in every corner, resulting in the growth of the data volume of e-commerce users. This paper mainly studies the data analysis of e-commerce users based on data mining technology. This paper introduces the research background and significance of e-commerce user behavior. On this basis, relevant technologies of e-commerce user behavior analysis are studied, including components of Spark platform and classification algorithm of relevant user behavior analysis model. Then, the advantages and feasibility of the improved model are verified through comparative experiments between the improved Spark XGBoost model and traditional machine learning method. The study in this paper provides a parallel method for the prediction of e-commerce user behavior, which can be applied to daily life as an effective method to predict e-commerce behavior.
Cross-border E-commerce Course Construction Based on Data Mining Algorithm
Authors: Deng Jianghua , Qing Haohua
Published: April 2021

In recent years, Cross-border(CB) e-commerce(EC) has developed rapidly in our country, and the government has also introduced a series of measures to promote the development of CB EC. In the era of big data, recommendation systems are widely used in CB EC platforms, and more and more attention is paid to its personalized recommendation technology. Based on this, this paper studies the application of data mining algorithms in the construction of CB EC courses, which has certain guiding significance for the current market and enterprise development. By taking two CB EC companies as the research object, this paper uses data mining algorithms to mine and analyze the relevant data of these two EC companies, and find that a platform with a data mining system has more advantages in researching customer shopping preferences;comparing people’s satisfaction with EC companies with data mining systems and traditional companies, 83% of the participants in the experimental group were satisfied, of which 60% were very satisfied, while only 59% in the control group of people are satisfied.This shows that data mining algorithms are of great significance to the development of CB EC, and it also points out a new direction for the construction of CB EC courses, such as CB EC based on data mining algorithms. On the one hand, this will also play a positive role in the future development of CB EC.
Application of Data Mining Technology in the Analysis of E-commerce Emotional Law
Authors: Zhang Shuyue
Published: April 2021

The rapid development of the Internet has brought opportunities for enterprises. Data mining is becoming more and more important in e-commerce websites. In e-commerce, data mining is helpful to discover the development trend of the industry and help enterprises make correct decisions. This paper summarizes the current data mining methods of e-commerce, describes the application of e-commerce website in some data mining applications, classifies its objects, and analyzes the functions of network data mining. It is an important tool to support the experimental research and design of e-commerce system oriented to emotion. The technology of Intelligent Emotion Recognition Based on modern mathematical model and the simulation model of emotion oriented intelligent e-commerce system based on computational intelligence are proposed. For the future of e-commerce practice to provide a reference, especially the development and application of software. The progress in this field is outstanding.
RETRACTED: Innovation Strategy of Film and Television Creation under the Background of Big Data
Authors: Chen Dan , Yang Fan
Published: April 2021

With the advent of the information age, the role of knowledge resources in modern social production is expanding. In today’s era, people have mastered a variety of information storage, processing and query methods, thus greatly expanding the scope of automation. At the same time, people have higher and higher requirements for the film and television drama, not only for the rich content and plot, but also for the actor’s acting skills. Through big data, we can analyze the types of movies popular with the public, so that the market can create films and TV programs according to their favorite degree of movies. Based on this, this paper analyzes the current situation of China’s film and television drama, combined with the background of the era of big data, studies the innovation of film and television creation. In this paper, a questionnaire survey is conducted to analyze the 10 most popular Chinese movies on Douban in 2019. The experimental results show that the public agree with the film quality in 2019. At the same time, among the favorite film types, the public’s love for inspirational movies and TV plays was the highest, reaching 86%, followed by social ethics drama with 84%, and youth idol drama with the lowest love rate of only 52%.
Power User Sensitivity Analysis and Power Outage Complaint Prediction
Authors: Ding Jiahan , Shi Yunchao , Zhu Ruiqian , Wei Xiaoxiong , Chen Bo , Yu Jianfeng
Published: April 2021

The sensitivity of power users is an important basis for improving the quality of power customer service and refining customer service content. In order to improve the accuracy of power user sensitivity classification, this paper optimizes and improves the decision tree algorithm based on the ant algorithm, builds a power user sensitivity analysis model, and verifies its effectiveness with simulation experiments, which provides a more powerful data reference for improving the quality of power user services and other tasks. Based on the analysis of the sensitivity of power users, combined with the analysis of related characteristic data of power outages, this paper predicts the probability of power user outage complaints, hoping to provide data reference for improving power user satisfaction and reducing power outage complaints.
Research on Tower Bolt Identification Technology Based on Convolution Network
Authors: Ge Liang , Xu Jing , Wu Xiao Lin , Hou Rong Jin , Shi Chang Jun , Jia GaoYi
Published: April 2021

Aiming at the fact that tower bolts are easily affected by factors such as occlusion during the identification process, an improved FasterR-CNN bolt identification method is proposed. In this method, in view of the small size of the detection target and the low resolution of the image, a residual network is added to the traditional FasterR-CNN to improve the efficiency of image recognition. Finally, the above scheme was proved through verification. The result shows that the above method is feasible.
Genetic Algorithm-Based Power System Information Security Risk Assessment Method
Authors: Huang Fangneng , Yong Mei , Jian Zhou
Published: April 2021

With the innovation and application of new-generation information technologies such as cloud computing, Internet of Things, and mobile Internet, the importance of information security issues has become more prominent. As an important industry linking people’s livelihood, the electric power industry will affect the safe operation of electric power once an information security incident occurs, and even lead to a large-scale blackout. Therefore, studying the information security risk assessment model of electric power enterprises has important theoretical value and practical significance. The purpose of this paper is to study the method of power system information security risk assessment based on genetic algorithm. This paper establishes a security risk quantification model, determines the operating parameters of the genetic algorithm in specific operations based on some literature cases, and compares the results in the literature with those in this paper. Based on a large number of investigations and interviews, this paper conducts research on information security risks, comprehensively considers the threats and vulnerabilities of the system, and analyzes the important impact of information security risks from the internal management, personnel, technology, and systems of electric power enterprises. Factors, the establishment of a power enterprise information security risk evaluation index system, this paper obtains data from actual system evaluation cases, uses multiplication, matrix method, and the genetic algorithm proposed in this paper to calculate the risk quantification model calculation results, and verifies the genetic algorithm the rationality and feasibility of combining with risk assessment, this method has practical application value. Experimental research shows that the risk value calculated by the genetic algorithm based on the risk quantification model proposed in this paper is more than 70% similar to the results calculated by the multiplication method and the matrix method, which proves that the model and calculation method established in this paper can be used. Applied to the specific evaluation implementation process.
Application of Intelligent Information Technology in the Reform of Hybrid Teaching Courses in Colleges and Universities
Authors: Huang Yanyan , Yao Jinxiu , Huang Guanhang
Published: April 2021

With the rapid development of information technology and the deepening of teaching information reform, hybrid teaching is becoming the focus and development direction of current higher vocational education reform. By integrating network information technology into traditional classroom teaching, hybrid teaching overcomes the shortcomings of traditional classroom teaching, gives full play to the advantages of information technology, and improves the quality of teaching. According to the characteristics of Modern educational Information and Technology course in higher vocational colleges, this paper studies the teaching reform of online and offline Modern educational Information and Technology. 50 questionnaires were sent out and the results were analyzed.
Analysis of Internal Threat Detection Methods of Power Grid under the Characteristic Cloud Computing Model
Authors: Li Jing , Huang Jie , Liu Fen
Published: April 2021

Advances in science and technology are driving the development of society, and with the development of society, our power grid coverage has become more extensive, which has solved the power supply problem in most parts of the country. As an important infrastructure of the country, the safety and nursing work of the power grid industrial control system is very important. However, the increase of coverage makes the maintenance and management of the power grid itself more difficult, and the internal threat of the power grid can not be ignored. To get a clearer understanding of the methods and patterns we use in terms of threats within the grid, this article will analyze the objects we are studying us using cloud computing simulation methods, and then analyze the results of the data. The experimental results show that the detection method of internal threat of power grid is still in the preliminary stage of development in our country.
Research on Automatic Labeling of Contact Profiler Based on Big Data
Authors: Li Qing
Published: April 2021

This article mainly studies the problem of labeling various parameter values of the contact profiler. The contour size of the instrument is analyzed by the least square method for the mathematical theory, and the data analysis information of each line and arc is established based on the background of big data. As a result, the corresponding mathematical model is established to realize the labeling of outline dimensions. First, perform precision processing on the horizontal data of the workpiece, use Excel software to make a scatter diagram, use the difference method to quickly search and locate the special inflection point coordinates, borrow these feature points to obtain the dimension of each data in turn, and combine the equations of straight lines and circles Find out the width and radius of the notch, find out the various indicators in turn, and finally sort and summarize them through the table. Next, the tilt data and the original data given for the workpiece are represented by a scatter graph. Using coordinate rotation and translation, the tilt angle is 7°24’59” clockwise and the radian is -0.13017. Compare the two sets of data After the difference, finally use segmented fitting to carry out horizontal correction.
Based on Grey Correlation Theory and BP Neural Network Model Algorithm, the Construction of the Ideological and Political Education Evaluation System of New Media in Colleges and Universities
Authors: Lu Chunyan
Published: April 2021

Every university today has its own new media management system. They manage the school’s microblogs, shaking, fast hands. The official account number of each platform, such as Beep Mile. However, with the intricacies of network information and the advance of the times, we should be alert to the influence of the color revolution, believe in the spirit of the red revolution, and promote the development of patriotism education. To this end, we need to make a better promotion of the socialist spirit and Xi Jinping’s new eraist development spirit in colleges and universities. In this regard, the purpose of this paper is to use gray correlation theory and BP neural network model algorithm to build a new media ideological and political education evaluation system. We fully protect the privacy of each teacher and student in order to get a real degree of ideological awareness, using gray correlation theory and BP neural network model algorithm to construct the system. Use anonymous whistleblowers and anonymous mailboxes to point out our political problems in order to better promote the construction of the system. The results show that there are about 20% of the students. Show that you don’t know enough about patriotic teachers. And said he will self-strengthen patriotism education in the future, believe in the Party and the country.
Construction and Effect Evaluation of Roaming System Based on Big Data
Authors: Wang Jingshuang
Published: April 2021

With the rapid development of science and technology, people’s living standards are slowly improving. Simple material conditions can no longer meet people’s spiritual needs. People use various methods to obtain spiritual and soul satisfaction. Therefore, Internet products gradually penetrate the public. There are no exceptions in all aspects of life, such as work, study, making friends, and entertainment. However, there are very few animation products, which cannot meet the strong needs of the majority of animation fans. Due to the popularity of the Internet and economic development, traditional economy is no longer the main way for consumers. In order to meet the individual needs of consumer groups, the animation industry will provide diversified and customized needs. The later development trend in this area is also animation. The industry’s indispensable and main development direction is to integrate everyone’s different hobbies and personalities into the animation industry, and develop an animation system with simple pages, rich functions and diversified content.