bluuebunny/crossref_metadata_2025 huggingface dataset:
An x-ray crosstalk correction method using FCNN for a novel energy resolving scheme in spectral CT
Authors: Shi Zaifeng , Wang Ziju , Kong Fanning , Cao Qingjie , Wang Ning , Qi Junyu
Published: April 2021

Spectral computed tomography has great potential for multi-energy imaging and anti-artifacts. The complete absorption-based energy resolving scheme of x-rays has been used for the integrity of detected information. However, this scheme is limited by the fact that the detector pixel thickness is high and fixed. Here, an energy resolving scheme is proposed using the crosstalk correction method for the incomplete absorption detection of x-rays. A fully connected neural network (FCNN)-based method was used to correct the difference caused by internal x-ray crosstalk of the edge-on detector. The energy and spatial features of the data which is collected in layers were combined to establish the mapping between the ideal data and the data with crosstalk at the pre-processing stage. Thereafter, to reconstruct the stable and highly accurate energy-resolving equations, the layers with low relative energy difference were selected and grouped together to reduce the accumulation difference. The experiment results demonstrate the feasibility of this energy resolving scheme. The differences caused by crosstalk can be suppressed through the proposed FCNN-based method. The resolving accuracy can be further improved by grouping more layers at forward positions in the pixel. Moreover, this improvement can be observed in the reconstructed images with reduced artifacts and improved quality.
Space-charge induced particle reflection between hybrid AC/DC biased electrodes
Authors: Lafleur T.
Published: April 2021

Space-charge limited current flow between DC biased electrodes is a widely applicable problem in many areas of physics. Recently, radio-frequency biasing, together with DC self-bias formation, has been studied as a new concept for the extraction of charged particles from an upstream plasma source. Here, we compare particle extraction between systems using this hybrid AC/DC biasing, with conventional DC biased electrodes, and identify important similarity parameters. The injection current first leading to particle reflection strongly depends on the applied AC frequency and voltage magnitude, as well as the initial particle injection velocity, and is in general lower than the DC case. For injection currents above the AC limit, the system becomes unstable, and self-excited space-charge oscillations are generated. A critical parameter is the ratio of the average particle transit time between the electrodes to the AC period, γ = t
L/T. As long as γ ≫ 1, the onset of particle reflection can be sufficiently delayed that the extracted current approaches the DC limit.
Localized-state ensemble model analysis of InGaN/GaN quantum well structures with different dislocation densities
Authors: Arteev Dmitri S , Sakharov Alexei V , Zavarin Evgenii E , Lundin Wsevolod V , Rzheutski Mikalai V , Lutsenko Evgenii V , Tsatsulnikov Andrey F
Published: April 2021

The influence of dislocations on luminescence of InGaN/GaN multiple quantum wells was investigated by temperature-dependent and time-resolved room-temperature photoluminescence measurements and analyzed via localized-state ensemble model. The results show that dislocations decrease non-radiative recombination time and do not affect either radiative recombination time or non-radiative recombination mechanism. Moreover, dislocation-related broadening, increasing linearly with increased dislocation density, was found to take place. However, a significant part of spectral width (∼55 meV) is not defined by either dislocation-induced or alloy- and thermally-induced broadening, revealing the existence of other broadening mechanisms (e.g. carrier–carrier scattering-induced broadening).
Screen-printed conductive pattern on spandex for stretchable electronic textiles
Authors: Lim Taehwan , Kim Hee Jin , Zhang Huanan , Lee Sohee
Published: April 2021

Current stretchable conductive fibers used for electronic textiles have received significant attention, but the fiber-based electronic textiles restrict the elaborate patterning due to the limited weaving process. To fabricate electrical circuits on textiles directly to ultimately combine various electronic devices, different approaches would be required to allow the pattern printing onto the stretchable fabric. Here, screen-printing was taken to prepare a highly durable, stretchable conductive pattern on spandex with elastic recovery and high washing durability. A zigzag pattern with the right angle was selected as an optimal shape to prevent the failure of conductive paste from the spandex after stretching cycles. The pattern can successfully diverge the stress by stretching to the

1

/

2

of applied one, which enables the conductive characteristic to maintain from the stress under the range of elastic recovery. Another key issue of wearable electronic textiles is the ability to operate under mechanical stress. Encapsulation is an easy modification method to allow the electrical characteristic of the conductive pattern on the spandex when stretched. We confirmed the pattern after encapsulation could act as conductive wires under the stretching deformation of 18% that was much higher than that of the pattern without encapsulation (7%). Consequently, the results suggest potential and further modification methods to fabricate high durable, stretchable electronic textiles.
Theoretical analysis and experimental validation of frequency-moldable electrostatic energy harvesters biased with a high elastic electret film
Authors: Ma Xingchen , Yang Xiaoya , Ding Chuan , Zhang Xiaoqing , Dai Ying , He Pengfei
Published: April 2021

It is an effective strategy to improve the power output of energy harvesters by adjusting resonance frequency of the devices to adapt to complex frequency distribution of vibrations in environments. This paper presents mechanical analysis and experimental validation of a kind of light-weighted, flexible electrostatic vibrational energy harvesters with a customizable resonance frequency range. Such energy harvester consists of a stretchable electret beam, whose high elasticity was achieved by introducing a corrugated structure, an arc-shaped counter electrode, and a light seismic mass. The resonance behavior of the device can be simulated by the corrugated beam with flexible support at both ends. Mechanical analytical modeling allows one to predict resonance frequency of energy harvesters, making the devices potentially interesting for customizable resonance harvesting and broad bandwidth energy harvesting, and thus to satisfy actual application scenarios. The experimental results are in agreement with the theoretical prediction. For a device with an initial size of 15 × 10 × 9 mm3 and a seismic of 0.06 g, by modulating the length of the fluorinated polyethylene propylene electret beam from 15 to 35 mm, a customizable resonance frequency ranging 14–60 Hz, and the normalized output power up to several 100 μW was achieved, demonstrating great superiority.
Fiber-solid hybrid end-pumped Nd:YVO4 picosecond amplifier with high beam quality
Authors: Liu Minzhe , Liu Menglin , Yan Wei , Yang Dewang , Wang Lisha , Sun Songsong , Jia Zhongqing , Zhai Ruizhan , Wang Yong , Zhao Kun
Published: April 2021

In recent years, high-brightness picosecond lasers have been widely used in high-end precision processing, spectroscopy and high harmonic generation. To obtain higher power and excellent beam quality, it is an ideal solution to add a high-gain amplifier to the back end of the high-stability picosecond fiber or solid seed. In this article, a fiber-solid hybrid end-pumped Nd:YVO4 picosecond laser system with high beam quality is demonstrated. The influence of the self-developed picosecond fiber seed output characteristics on solid amplifiers has been studied in detail, including the energy, repetition rate and spectrum. After two-stage traveling wave amplifiers, the maximum 155 μJ single pulse energy is obtained with 20 kHz repetition rate, and the corresponding peak power reaches 14.9 MW with 10.4 ps pulse duration. Meanwhile, the beam quality factors under the maximum 18.4 W average power are measured to be M
x
2 = 1.27 and M
y
2 = 1.25, respectively.
Fabrication of sulfur-doped cove-edged graphene nanoribbons on Au(111)*
Authors: Yang Huan , Gao Yixuan , Niu Wenhui , Chang Xiao , Huang Li , Liu Junzhi , Mai Yiyong , Feng Xinliang , Du Shixuan , Gao Hong-Jun
Published: April 2021

The on-surface synthesis from predesigned organic precursors can yield graphene nanoribbons (GNRs) with atomically precise widths, edge terminations and dopants, which facilitate the tunning of their electronic structures. Here, we report the synthesis of novel sulfur-doped cove-edged GNRs (S-CGNRs) on Au(111) from a specifically designed precursor containing thiophene rings. Scanning tunneling microscopy and non-contact atomic force microscopy measurements elucidate the formation of S-CGNRs through subsequent polymerization and cyclodehydrogenation, which further result in crosslinked branched structures. Scanning tunneling spectroscopy results reveal the conduction band minimum of the S-CGNR locates at 1.2 eV. First-principles calculations show that the S-CGNR possesses an energy bandgap of 1.17 eV, which is evidently smaller than that of an undoped cove-edged GNR (1.7 eV), suggesting effective tuning of the bandgap by introducing sulfur atoms. Further increasing the coverage of precursors close to a monolayer results in the formation of linear-shaped S-CGNRs. The fabrication of S-CGNRs provides one more candidate in the GNR toolbox and promotes the future applications of heteroatom-doped graphene nanostructures.
Influence of sulfur doping on the molecular fluorophore and synergistic effect for citric acid carbon dots*
Authors: Cao Guohua , Wei Zhifei , Yin Yuehong , Fu Lige , Liu Yukun , Qiu Shengli , Zhang Baoqing
Published: April 2021

In citric acid-based carbon dots, molecular fluorophore contributes greatly to the fluorescence emission. In this paper, the nitrogen and sulfur co-doped carbon dots (N,S-CDs) were prepared, and an independent sulfur source is selected to achieve the doping controllability. The influence of sulfur doping on the molecular fluorophore was systematically studied. The introduction of sulfur atoms may promote the formation of molecular fluorophore due to the increased nitrogen content in CDs. The addition surface states containing sulfur were produced, and S element exists as –SO3, and –SO4 groups. Appreciate ratio of nitrogen and sulfur sources can improve the fluorescence emission. The photoluminescence quantum yields (PLQY) is increased from 56.4% of the single N-doping CDs to 63.4% of double-doping CDs, which ascribes to the synergistic effect of molecular fluorophores and surface states. The sensitivity of fluorescence to pH response and various metal ions was also explored.
Research on magnetic fields of galaxies using RZ-model
Authors: Mikhailov Evgeny Alexandrovich , Pushkarev Vasilii Vladimirovich
Published: April 2021

The generation of magnetic fields of galaxies is usually described by the dynamo mechanism. This process is characterized by the Steenbeck – Krause – Rädler equation, which is the result of averaging the magnetohydrodynamics equations by distances which are associated with the size of turbulent cells in the interstellar medium. This equation is quite difficult to solve both from an analytical and numerical point of view. For galaxies, the no-z approximation is widely used. It describes the magnetic fields in thin discs. For such objects, where it is important to study the vertical structure of the field, it is not very applicable, so it is quite useful to adopt the RZ-model, which takes into account the dependence of the distance from the equatorial plane. During our research we have obtained the critical values of the dynamo number for galaxies with large half-thickness. We have also described typical z-structure for the magnetic field. Moreover, we have demonstrated that it is possible to generate dipolar magnetic fields.
FUV and NIR size of the HI selected low surface brightness galaxies
Authors: Cheng Cheng , Du Wei , Xu Cong Kevin , Cao Tian-Wen , Zhang Hong-Xin , Huang Jia-Sheng , He Chuan , Li Zi-Jian , Wu Shu-Mei , Xu Hai , Dai Y. Sophia , Shao Xu , Musin Marat
Published: April 2021

How low surface brightness galaxies (LSBGs) form stars and assemble stellar mass is one of the most important questions related to understanding the LSBG population. We select a sample of 381 HI bright LSBGs with both far ultraviolet (FUV) and near infrared (NIR) observations to investigate the star formation rate (SFR) and stellar mass scales, and the growth mode. We measure the FUV and NIR radii of our sample, which represent the star-forming and stellar mass distribution scales respectively. We also compare the FUV and H band radius-stellar mass relation with archival data, to identify the SFR and stellar mass structure difference between the LSBG population and other galaxies. Since galaxy HI mass has a tight correlation with the HI radius, we can also compare the HI and FUV radii to understand the distribution of HI gas and star formation activities. Our results show that most of the HI selected LSBGs have extended star formation structure. The stellar mass distribution of LSBGs may have a similar structure to disk galaxies at the same stellar mass bins, but the star-forming activity of LSBGs happens at a larger radius than the high surface density galaxies, which may help to identify the LSBG sample from the wide-field deep u band image survey. The HI is also distributed at larger radii, implying a steeper (or not) Kennicutt-Schmidt relation for LSBGs.
Machine learning-based personalized subthalamic biomarkers predict ON-OFF levodopa states in Parkinson patients
Authors: Sand Daniel , Rappel Pnina , Marmor Odeya , Bick Atira S , Arkadir David , Lu Bao-Liang , Bergman Hagai , Israel Zvi , Eitan Renana
Published: April 2021

Objective. Adaptive deep brain stimulation (aDBS) based on subthalamic nucleus (STN) electrophysiology has recently been proposed to improve clinical outcomes of DBS for Parkinson’s disease (PD) patients. Many current models for aDBS are based on one or two electrophysiological features of STN activity, such as beta or gamma activity. Although these models have shown interesting results, we hypothesized that an aDBS model that includes many STN activity parameters will yield better clinical results. The objective of this study was to investigate the most appropriate STN neurophysiological biomarkers, detectable over long periods of time, that can predict OFF and ON levodopa states in PD patients. Approach. Long-term local field potentials (LFPs) were recorded from eight STNs (four PD patients) during 92 recording sessions (44 OFF and 48 ON levodopa states), over a period of 3–12 months. Electrophysiological analysis included the power of frequency bands, band power ratio and burst features. A total of 140 engineered features was extracted for 20 040 epochs (each epoch lasting 5 s). Based on these engineered features, machine learning (ML) models classified LFPs as OFF vs ON levodopa states. Main results. Beta and gamma band activity alone poorly predicts OFF vs ON levodopa states, with an accuracy of 0.66 and 0.64, respectively. Group ML analysis slightly improved prediction rates, but personalized ML analysis, based on individualized engineered electrophysiological features, were markedly better, predicting OFF vs ON levodopa states with an accuracy of 0.8 for support vector machine learning models. Significance. We showed that individual patients have unique sets of STN neurophysiological biomarkers that can be detected over long periods of time. ML models revealed that personally classified engineered features most accurately predict OFF vs ON levodopa states. Future development of aDBS for PD patients might include personalized ML algorithms.
A point plasma model for linear plasma devices based on SOLPS-ITER equations: application to helium plasma
Authors: Tonello E. , Formenti A. , Alberti G. , Uccello A. , Passoni M.
Published: April 2021

Linear plasma devices represent an essential tool for nuclear fusion research, whereby understanding crucial aspects related to plasma-wall interactions or edge plasma behaviour. Simplified models are of great importance to complement and integrate experimental and simulation results of complex systems such as plasmas in linear machines, because they are fast and simple to employ. In this work, we present a global volume-averaged (0D) model for plasma investigation in linear machines. The 0D model equations are based on the space integration of the state of the art edge plasma model implemented in the SOLPS-ITER code. Comparisons between helium plasmas described with 2D simulations performed with SOLPS-ITER and with the 0D model highlight that contributions often neglected in tokamak edge models, e.g. electron-neutral excitation, may be relevant when describing weakly ionized plasmas in linear devices. The model is used to perform sensitivity studies with respect to several parameters and to analyse the time evolution of the system, leading to the identification of two relevant time scales governing the system. Lastly, a comparison of 0D results with experimental data from the linear device GyM is performed, showing satisfactory agreement. Our methods and results provide crucial interpretative keys in the investigation of the physics of edge plasmas.
3D radiation, density, and MHD structures following neon shattered pellet injection into stable DIII-D Super H-mode discharges
Authors: Sweeney R. , Baylor L.R. , Eidietis N.W. , Granetz R.S. , Herfindal J.L. , Hollmann E.M. , Hu D. , Lehnen M. , Moyer R. , Raman R. , Shiraki D. , Strait E.J. , DIII-D Team
Published: April 2021

Six nominally repeat neon shattered pellet injection (SPI) shutdowns of stable DIII-D Super H-modes are studied to understand the 3D properties of the radiation and impurity transport. The radiation efficiency and radiation peaking determine whether first wall melting is expected following disruption mitigation in ITER. Previous studies make use of axisymmetric approximations to infer radiation efficiencies, but validating the high efficiency required by ITER necessitates improved accuracy, and this work contributes by exploring the 3D radiation and density structures that will inform forward modeling. When the neon shatter plume produced by the SPI reaches the plasma edge, m/n = 3/1 and 2/1 island O-points are observed to align with the injection trajectory in five out of six cases, suggesting that the injected material seeds the island O-points. Field aligned neon structures emitting Ne-I line radiation drift at 1 km s−1 in the ion diamagnetic drift direction during the pre-thermal quench, tracking the motion of the m/n = 2/1 island O-point. Neon fragments penetrate to the q = 2 surface by the time of the thermal quench. Techniques to constrain the 3D emissivity are explored, and one method constrains a 3D flux tube that is consistent with the radiation data, and when mapped to the interferometers, intersects the lasers that measure the highest density. The resulting structure derived from the radiation measurements exists near the 2/1 island X-point. In five repeatable discharges, the peak of the radiation in the toroidal direction exists in a 120° toroidal sector where the injection occurs, in contrast with the outlier discharge where the toroidal peak exists in the complementary 240° toroidal sector far from the injector, and where a 50% lower density rise is observed. The n = 1 phase behavior is markedly different in the outlier discharge, suggesting a possible dependence of the radiation structure and the assimilation efficiency on MHD.
The competency of vocational graduates in the fields of mechanics and workshop management according to the needs of the automotive motorcycle industry
Authors: Wahyudi K S , Sulistyo B , Budiman A , Arifin Z
Published: April 2021

This study aims to determine the mechanics competence and workshop management needs of the competencies in the vocational education engineering curriculum and the motorcycle business according to the automotive motorcycle industry. Research subjects were eight motorcycle automotive companies in the field of production and services. The data of the study were collected by using a questionnaire survey and analyzed by quantitative descriptive statistics with percentages. The results of the study showed that the needed competencies according to the Automotive Motorcycle Industry were mechanics competence and workshop management competence. The vocational education curriculum produced 39 mechanics competencies and 18 workshop management competencies. There were 37 basic competencies in mechanics competence in the very needed category and 2 basic competencies in the sufficiently needed category. Workshop management competencies found 18 basic competencies that were all needed by the industry. Competency from the mechanics field and workshop management had competencies in the very needed category relatively. In total there were 15 competencies that not exist yet in the curriculum which taught in vocational education.
Detection of ibuprofen and aspirin on silver nets by surface enhanced Raman scattering (SERS) spectroscopy
Authors: Yarantseva N D , Belyatsky V N , Shleiko E V , Osotskaya E S , Burko A A , Dolgiy A L , Girel K V , Bandarenka H V
Published: April 2021

In this work, surface enhanced Raman scattering (SERS) spectra of aspirin and ibuprofen molecules adsorbed on the surface of silver nets sputtered on porous silicon were collected and analyzed. The bands in the SERS-spectra were correlated in accordance with the type of molecular vibration. Predominantly chemical adsorption of the indicated drugs on the SERS-active substrate through oxygen was observed. It was established that the Raman spectroscopy combined with the SERS-active silver nets makes it possible to detect the aspirin and ibuprofen at 10-6 M concentration.
Simulation of Evacuation in Crowded Places Based on BIM and Pathfinder
Authors: Zhang Hui , Long Hao-cheng
Published: April 2021

Densely-populated public areas with high concentration of people have many potential hazards for its high difficulty in evacuation. Once an emergency occurs, it is easy to cause crowd panic and casualties. Therefore, research on the building characteristics and evacuation methods for densely-populated areas can provide reference and the emergency plan preparation for emergencies. Taking the central building of the Women and Children’s Health Hospital in a certain city as the research object, the simulation and analysis of the evacuation process in the three-dimensional building scene are carried out based on BIM modeling and Pathfinder simulation software. The results show that due to the special proportion of people in the building, appropriately increasing the width of the evacuation doors near the evacuation stairs and fire elevators, clarifying the responsibilities of medical staff, and controlling the escape speed of people in different areas can significantly improve the evacuation efficiency.
A Fast Traffic Sign Detection Algorithm Based on Modified YOLOv3
Authors: Dou Huazhou , Zhang Haiyun , Li Bo
Published: April 2021

Traffic sign detection is an important component of both automatic driving systems and driver assistance systems. However, it is still a big challenge to detect traffic signs quickly and accurately. In this paper, in order to solve this problem, a lightweight algorithm based on YOLOv3 is proposed, which can achieve real-time and accurate traffic sign detection. Firstly, a new lightweight backbone network is proposed by modifying the darknet53 network, which is the original backbone network of YOLOv3. Secondly, in order to further speed up the detection time, depthwise separable convolution is introduced into the proposed backbone network to further reduce the amount of parameters and computations of the model. Thirdly, dense connection is adopted to maintain the accuracy. Experiments on CCTSDB dataset illustrate that the algorithm has great real-time performance and accuracy, which is better than other state-of-the-art algorithms. The algorithm can detect a 416*416 image in only 3.4 milliseconds, which is 44.3% faster than YOLOv3. In addition, the mAP and F1 value are 97.2% and 91.6% respectively, which are better than YOLOv3.
A Traffic Interval Prediction Method Based on ARIMA
Authors: Lv Tiantian , Wu Yanqin , Zhang Le
Published: April 2021

The purpose of this paper is to construct an autoregressive integrated moving average model (ARIMA) for forecasting the traffic interval, which is helpful for the mobile industry to forecast the change of requirements for the peak of customer traffic, adjust the bandwidth dynamically, and improve the ability of active service. In this paper, the lateral time series analysis method is applied to analyze the data of the peak traffic of the uplink and downlink network from August to October of 2017 and 2018 to establish ARIMA prediction model for determining the parameters of it. MAPE method is used for model assessment and model diagnosis. Then, the optimal forecasting model is selected and the forecast error rate is calculated as the adjustment parameter of the forecast range. The model is used to forecast the customer flow range in the first three days of October 2019. Finally, the MIA method is proposed to compare with the LSTM algorithm and linear regression method for interval cumulative error comparison of traffic interval prediction. The result shows that the ARIMA (0,1,0) model has the lowest mean interval error rate of 15.5%, which proves the reliability of the model in predicting peak traffic interval.
Cross-Visual Attention Fusion Network with Dual-Constrained Marginal-Ranking for Visible-Infrared Person Re-Identification
Authors: Su Fang , Qi Meibin , Chen Cuiqun , Bo Tan , Jiang Jianguo
Published: April 2021

Visible-Infrared Person re-identification(VI-REID) is extremely important for night-time surveillance applications. It is a challenging problem due to large cross-modality discrepancies and intra-modality variations caused by different illuminations, human poses, viewpoints, etc. In this paper, we propose a cross visual attention fusion dual-path neural network with dual-constrained marginal ranking(DCAF) to solve the problem. First, we utilize cross-visual attention to learn discriminative feature of high-level semantic information in their respective modals. Second, in order to establish the relationship between modals, we fuse attentional weight of two modals and add it into backpropagation to obtain those regions that are distinctive for classification. Third, a dual-constrained marginal-ranking loss is introduced to narrow the gap between different networks and to learn strongly the similarities between two modals. Extensive experiments demonstrate that the proposed approach effectively improves the performance of VI-REID task and remarkably outperforms the state-of-the-art methods.
Multi-scene Representation Learning with Neural Radiance Fields
Authors: Fu Bofeng , Wang Zheng
Published: April 2021

Getting representations of multiple objects or scenes is a raising research topic in Machine Learning (ML) community. Here, we propose a multi-scene representation model that can learn the representation of complex scenes and reconstruct them in high resolution given novel viewing directions. Our method represents a single scene with fully-connected layers. Each set of fully-connected layers are controlled by hyper-networks for multiple scenes modeling. For each scene, we take 3D coordinates (x, y, z) and 2D view-point orientations (θ, ɸ) as inputs. A set of fully-connected layers output volume density and RGB values at given 3D spatial positions. Then, we render the output volume density and RGB values along the camera rays into images using volume density rendering techniques. During training process, we optimize a continuous volume scene function with a small amount of input viewing directions. By designing versatile embedding module and multi-scene representation networks, our model can render photographic images with novel viewing directions for different complex scenes. Experiment results demonstrate the neural rendering and multi-scene representation abilities of our model. Several thorough experiments show that our method outperforms previous model on both reconstruction precision and scenes generation ability from novel viewing directions.
Application of Artificial Intelligence in Cultural Heritage Protection
Authors: Li Junli
Published: April 2021

With the continuous development of science and technology, artificial intelligence has become the subject of extensive discussion in recent years. Artificial intelligence technology uses deep learning to apply this, it shows the trend of high development, such as the previous Go game that shocked the world, the fact that artificial intelligence “Alphago” defeated the world Go champion, people realized that the development of artificial intelligence exceeded people Imagination. Various social industries are gradually introducing artificial intelligence technology to give them new vitality for development. This is exactly how the cultural heritage protection process should be combined with artificial intelligence technology. Cultural protection should enter the digital era, using virtual reality technology and applying segmentation algorithms to segment the prospects of cultural heritage. Complete all aspects of cultural heritage protection without applications. This article introduces the process of artificial intelligence in the protection of cultural heritage using virtual reality technology to realize the digitization of cultural heritage, realizing that the protection of cultural heritage keeps pace with the times. The experimental results of this paper show that the virtual reality technology combined with artificial intelligence is applied to the importance of cultural heritage protection, which improves the vitality of cultural heritage and more perfectly shows the charm of cultural heritage application.
Intelligent Development of Safety Production System under 5G+ Industrial Internet
Authors: Zhang Tianhao
Published: April 2021

As an opportunity for countries around the world to revitalize the manufacturing industry and the transformation and upgrading of our country’s manufacturing industry, the production safety system is an opportunity for the development of advanced information technology, manufacturing technology and artificial intelligence. At this stage, intelligent manufacturing has set off a global research boom. This article mainly introduces the research on the intelligent development of the safety production system under 5G+ Industrial Internet. Through the intelligent research on the safety production system, the development of the early warning system model of the safety production system is carried out. This paper uses 5G + Industrial Internet combined with intelligent manufacturing technology to establish an early warning model. Increase the efficiency of the production system by 34%, greatly reducing the possibility of safety accidents. Based on the traditional safe production system, facing the development needs of intelligent manufacturing technology, and taking the training goal of advanced skilled talents as the direction, this paper deeply analyzes the requirements and characteristics of the training model for the intelligent manufacturing system, and constructs the corresponding Intelligent manufacturing safety production system.
Application of Computer Technology in Life under the Background of Big Data
Authors: Ma Ziqiang , Zhang Guidong , Wei Danni
Published: April 2021

Since we entered the 21st century, because of the arrival of the information age, the speed of social development has become faster and faster. The unprecedented development of computer technology is also an important cornerstone of the rapid development of society. Computer technology has penetrated into all areas of our production and life. Nowadays, computer technology has become an inseparable and important tool for us. Computer technology has not only gradually changed people’s way of life, but also changed people’s way of thinking. It is precisely because of the continuous development of computers that many earth-shaking changes have taken place in our daily lives. It can be said that the reason for the rapid development of contemporary society is based on the computer. This article uses literature research methods and other methods to study the changes in all aspects of our lives by computers in contemporary society, and to study the changes in our lives by computer technology. Research has shown that computer technology has greatly improved our life efficiency by 67%.
RETRACTED: The Strategy of Statistical Analysis of Audit Data in the Era of Big Data is Explored
Authors: He Hongfei
Published: April 2021

In the current era, audit data statistical analysis strategies have a variety of forms, we can also use a variety of forms to explore. But because the large amount of information has made things complex and changeable, it presents us with a lot of challenges. Since audit data is one of the necessary areas for socialist construction and development, we should value them. Therefore, the purpose of this paper is to use advanced algorithms to analyze the statistical analysis strategy of audit data in the era of big data. Based on the privacy and security provisions of the data age, this paper uses the data flow analysis algorithm to carry out comprehensive modeling analysis to detect the experimental results in order to improve the accuracy of the analysis. The experimental results show that using this algorithm to perform appropriate modeling operations can improve the speed and correctness of statistical analysis of audit data.
Image Processing Technology in Agriculture
Authors: Xu Nan
Published: April 2021

The emergence of image processing technology has greatly promoted the development of agriculture in China. By describing the application status of image processing technology in agriculture and its impact on agricultural output value, it is convenient for people to understand the importance of image processing technology for agricultural development. Based on this, this paper analyzes the application of image processing technology in the field of agriculture. Firstly, this paper analyzes the application of image processing technology in agricultural field. Secondly, in order to highlight the application effect of image processing technology in agricultural field, this paper applies image processing technology and traditional machine recognition technology to crop pest detection, and analyzes their effects. The results showed that the recognition rate of the traditional machine recognition technology was 65%, 71%, 74%, 63%, 64% and 62% respectively. The results show that the recognition rate of this method is 86%, 89%, 91%, 83%, 78% and 79%, respectively. It can be seen that the detection method of image processing technology is better in the detection of crop diseases and insect pests.
Application Analysis of Computer Technology in E-Commerce Network Consumption Interaction
Authors: Feng Lin
Published: April 2021

As companies pay more attention to online marketing, e-commerce has emerged, and it has quickly become the fashion consumers want, and more and more consumers have joined the online market. This article aims to study the application and analysis of computer technology in e-commerce network consumption interaction. The premise of this article is to analyze the classification requirements of computer marketing data in the e-commerce sector, and introduce the application of computer technology in e-commerce consumption in detail, and analyze the interactive role of computer technology in e-commerce consumption. This article defines the behavior of online consumers, and puts forward an e-commerce service quality relationship model, cases, and mathematical statistics for application model experiments. The experimental results in this paper show that the correlation coefficients between the six measurement elements of e-commerce service quality and the attitudes of online consumers are all positive. Among them, security and online consumer attitudes are significantly correlated at the p-value of 0.05.
Discussion on the Application of Computer Network in Intelligent City
Authors: Sun Qingli
Published: April 2021

Promoting the construction of intelligent city is a recognized viewpoint at home and abroad at present. Speeding up the construction of intelligent city is an effective grasp to further promote the governance of intelligent city in China and enhance the efficiency of government management. It is also an inevitable choice to innovate and optimize social and economic management, improve people’s livelihood public services, optimize modern industrial structure and promote economic structural transformation. A street lamp control system based on computer network is designed to analyze the application prospect of computer network technology in the future development of intelligent city. The system meets the basic requirements of 0.3 S and 80% in data transmission rate and success rate, respectively.
Application of Information Security in Image Encryption Processing Based on Big Data
Authors: Lv Haicui
Published: April 2021

With the widespread application of Internet technology, digital image encryption needs to be completed. But image is different from text, it has many inherent characteristics, such as large amount of image data, high redundancy, strong correlation between adjacent pixels. Therefore, the traditional symmetric encryption algorithm data encryption standard and advanced data addition standard will no longer be suitable for image encryption. Based on the concept of information security and image encryption, a new image processing method is proposed in this paper, which is based on the logistic tent sine composite algorithm model. The compression ratio is 75%. When the wrong key is input, the PSNR of the algorithm reconstruction result is only about 5dB, and the reconstruction error error is only 1, so the encryption effect of the model is better.
Application Computer Detection and Control System in Automobile Electronic Control System
Authors: Zhang Qiang , Guo Fengquan , Ma Chuanhai
Published: April 2021

The overall development of our society has promoted the development of computer technology, making the various performances of modern cars more perfect. The application of computer detection and control system technology in automobile electronic control devices can realize the intelligence and automation of automobile control. Now in the information age, computer perception and control technology are widely used in various social production fields, and are also widely used in automotive electronic control systems. Due to the emergence of automotive electronic control technology, automotive performance continues to improve, automotive safety performance improves, and automotive dynamic performance becomes stronger. In the automotive electronic control device, to understand the role of the computer detection and control system more specifically, it is necessary to fully understand and master the operation principle of the computer detection and control technology and the application range of the automotive electronic control device. In order to further improve the safety performance of the vehicle, in addition to the structural improvement of the vehicle itself and the addition of safety protection devices, it is a more effective method to perceive the surrounding environment and vehicle status during driving to achieve control. This article first introduces the computer detection and control technology, and analyzes the application program in the automotive electronic control device for reference.
Treatment Research on Mental Diseases of Brain Organic Mental Disorders Based on Big Data
Authors: Zhang Dongning , Men Ke , Ma Yonghong , Shi Mingjuan , Lei Jing , Zhang Hairui
Published: April 2021

In the field of medical research, even for the same disease, different scientific methods are usually used for research and experiments, but the final results are not the same. This article aims to analyze the treatment plan of organic mental disorder in mental illness based on the big data field of view and synthesize different existing research results. Based on the application of big data in medical experiments, this article uses big data technology to quantitatively analyze and process the related research results of brain organic mental disorders and the treatment data of similar cases, and use appropriate statistical methods to analyze multiple research results Perform quantitative, qualitative, systematic and standardized comprehensive analysis to study different treatment methods and treatment effects of organic mental disorders. The experimental results show that the treatment of organic mental disorders based on big data can observe and understand the patient’s condition in real time, and adjust the treatment plan in time. Compared with traditional treatment methods, the probability of detecting the condition in time has increased by about 13%, and the treatment effect on brain organic mental disorders has increased by about 15%.
Research on the Scheme of Multi-objective Planning Water Resources Optimization System Based on Network Model
Authors: Dai Liyuan , Zhang Cheng
Published: April 2021

Water resource scheduling is a key way to efficiently allocate regional water resources and enhance water use efficiency. This study takes the optimal scheduling of water resources in a certain area as an example. Aims to enhance the ecological, social and economic benefits in the scheduling area, and establish SD-MOP water resources optimal dispatch model. The results show that compared with the traditional SD model, the SD-MOP model predicts that the dispatch result in 2025 is closer to the actual water demand of the city, and saves the cost of 2.053 million yuan, and the pollutant discharge is only 0.891t, which brings considerable economic and environmental benefits.
Application of Internet of Things Technology in Garment Design
Authors: Guo Yan
Published: April 2021

Summary. The Internet of things is an information network with the ability to perceive objects comprehensively and to transmit information reliably and intelligently. The research and development of the Internet of things enable us to obtain real-time information on anything, and also make the economic development of our country slowly enter the track of healthy operation. It is of great strategic significance to speed up the transformation of industry to a higher level, to develop new industries and to take the high-end industrial road. With the development of economic globalization and information globalization, the Internet of things is developing very rapidly in various industries. In view of the problems of low inventory utilization, poor management information and low supply chain efficiency in clothing industry, the application of Internet of things in clothing industry is very important. At present, there are various problems in the sales system of clothing industry, so it is of great significance to apply the collaborative system to the brand clothing system under the Internet of things. Reduce 10% inventory, improve 60% supply chain quality, brand clothing development is of great significance.
Design of intelligent invigilator system based on artificial vision
Authors: Yan Bai , Mei Liu
Published: April 2021

Aiming at the problems of high packet loss rate and low intelligent degree in the current video monitoring system for examination invigilation, an intelligent invigilator system design method based on artificial vision is proposed in this paper. The system design is divided into hardware and software parts, including a / D circuit, clock circuit, video frame cyclic error correction and coding circuit, program loading circuit and output interface circuit. The experimental results show that the system can effectively reduce the video packet loss in the process of invigilation, improve the visual coverage of the invigilator area, and the system is highly integrated and intelligent.
Research on Logistics Distribution in E-commerce Environment Based on Particle Swarm Optimization Algorithm
Authors: Yufeng Zou , Wan Si
Published: April 2021

In order to improve the efficiency of logistics distribution under electronic commerce environment and reduce the logistics cost of distribution path length and time delay, to solve the problem of logistics distribution method at present larger and better convergence, a logistics distribution method in e-commerce environment is proposed based on particle swarm optimization algorithm, the characteristics of construction behavior the characteristics of logistics distribution under the e-commerce environment, the logistics distribution problem into a multi-objective optimization problem of particle swarm optimization. Based on the IOT technology, to establish a stable and efficient logistics supply chain structure, according to the interval distribution characteristics of e-commerce products to the distribution of the time interval of extraction group behavior of particles, the adaptive path characterized the amount of logistics distribution optimization and implementation under the environment of e-commerce logistics distribution path optimization, improve the coverage level of logistics and distribution of the simulation nodes. The results show that the method has stronger spatial search ability, it has higher convergence accuracy and faster search speed, and reduces the time cost and path overhead of logistics delivery.
Data Mining Analysis Research on Intelligent Application of Cloud Accounting——Taking Cloud Accounting and Financial Sharing Center as an Example
Authors: He Qinwei
Published: April 2021

This paper takes the cloud accounting and financial sharing center as the research object. It firstly expounds and analyzes the current status of cloud accounting and financial sharing, its operation management, and data mining intelligence, and provides necessary theoretical support for subsequent optimization. Secondly, through field research, interviews and collection of relevant materials, the current status of cloud accounting and financial sharing center operation management is described and the problems are analyzed. During the description of the current status, problems that can be optimized based on the intelligent perspective of data mining are found. Including performance management, risk management and process management modules. It enriches the method and technical system of cloud accounting and financial sharing centre’s operation management optimization, contributes to the intelligence and automation of cloud accounting and financial sharing centre’s operation management, effectively reduces the cost of operation management of cloud accounting and financial sharing center, and improves the operational management quality.