bluuebunny/crossref_metadata_2025_split huggingface dataset:
Does Caffeine Intake Increase the Incidence of Tinnitus? A Systematic Review
Authors: Aljuaid Sattam M. , Mirza Ahmad A. , Habib Lura A. , AlHarthi Lujain A. , Alansari Bashayer M. , AlQahtani Bashaier G. , Althobaiti Youssef A. 
Published: December 2020

Abstract
Introduction Excessive caffeine intake has been thought to be a contributory factor for tinnitus. However, there has been no systematic review to elucidate the causal relationship between caffeine intake and the incidence of tinnitus.
Objectives We performed the current review aiming at evaluating the evidence from the current literature for the relationship between caffeine intake and the incidence of tinnitus.
Data Synthesis Databases including PubMed, Scopus, and Google scholar were searched for relevant articles. A total of 142 studies were screened for eligibility, of which four articles met our inclusion criteria: two were prospective cohorts and two were cross-sectional studies. Although one study found no association between caffeine consumption and the incidence of tinnitus, an inverse relationship was reported by two population-based studies. Concerning patients with preexisting tinnitus, reduction of caffeine intake in a subset who consumed 150 ml to 300 ml/day of coffee yielded a favorable outcome in tinnitus severity. However, those with higher dose intake were less prone to have improvement in the severity of tinnitus.
Conclusion Although the current review was inconclusive, it appears that the incidence of tinnitus in previously unaffected individuals might be prevented by a high dose of caffeine intake. However, in preexisting tinnitus, a high dose of caffeine may adversely interfere with the efficacy of caffeine reduction.
Using deep neural network model to predict the plastic behaviour of DP780 steel under complex loading
Authors: Fu Zemin , Xiong Pengpeng 
Published: December 2020

Abstract
In the forming process of sheet metal, the sheet would be subjected to a complicated loading history. The change of pre-strain and the change of strain path might be concentrated in the sheet metal forming process. Through the tensile-tensile experiment of dual-phase (DP780) sheet steel, the flow stress-strain curve obtained shows the cross effect and permanent hardening behaviour. In order to predict the flow stress of DP780 sheet steel under different pre-strain, strain and strain paths, a deep neural network model is established. The data set is divided into training set, validation set and test set to train, verify and test the deep neural network model. The correlation coefficient between the test set prediction results and the experimental results is 98.88%, and the deep neural network model has great predictive ability. Simultaneously, the deep neural network accurately predicts the cross effect and permanent hardening behaviour of DP780 steel.
Research on Mobile Robot Positioning and Navigation System Based on Multi-sensor Fusion
Authors: He Cheng , Ma Renjie , Qu Huaying , Gu Runwei 
Published: December 2020

Abstract
The mobile robot integrates walking and operation functions, which can greatly expand the application range of traditional industrial robots. Aiming at the shortcomings of single sensor positioning under unknown indoor environment, such as large accumulated error and environmental limitations, a multi-sensor fusion positioning navigation system is designed to improve the positioning accuracy of autonomous navigation of mobile robots. The mobile robot uses the laser sensor as the core sensor to obtain two-dimensional information of the environment and realize real-time display of algorithms and interfaces such as map construction, positioning and navigation. The data results of the indoor navigation experiment show that the system has good robustness. At the same time, the feasibility and reliability of the system are verified.
Research on Supply Chain Management Information System of Foreign Trade Enterprises Based on Artificial Intelligence
Authors: Yan Zhang 
Published: December 2020

Abstract
Supply chain management plays an important role in the economic benefit growth and market competitiveness of foreign trade enterprises. At present, it is found from various information statistics that most foreign trade companies are still in exploratory for their understanding of supply chain management without establishing a complete system. Under the background of artificial intelligence (AI) technology, enterprises should actively introduce various high-end technical means, build scientific and technological systems and then enhance their core competitiveness. Companies can independently research and develop expert systems to collect various information and data, perform scientific reasoning, solve some economic problems, and expand the scope of trade.
The Influence and Countermeasures of Enterprise Marketing Activities under the Big Data Background
Authors: Nan Wang , Xiaochun Shi 
Published: December 2020

Abstract
The rapid development of Internet information technology has greatly changed people’s daily work and life patterns, and has produced a new type of economy-the network economy, which has brought new opportunities and challenges to traditional enterprise marketing methods. Enterprises can obtain a large amount of data information based on online sales. How to make full use of data information in the context of big data, seize opportunities, and actively transform has become a hot spot for many enterprises. The article will give a brief overview of the big data background and network economy, and analyze the impact of big data on enterprise marketing activities, and finally propose optimization strategies to help the relevant.
The Development of Machinery Manufacturing And the Application Analysis of Artificial Intelligence
Authors: Xingyu Chen , Chuntang Cai 
Published: December 2020

Abstract
Mechanical manufacturing and artificial intelligence (AI) are important applied technologies in the new era, and they are also part of the market structure. Under the background of the modern information technology, large-scale machinery manufacturing has undergone tremendous changes. Enterprises have begun to use AI technology and discard traditional manufacturing methods, and improve work efficiency. AI is a digital system with an important guiding role for machinery manufacturing. Enterprises should skillfully apply AI technology to promote the machinery manufacturing.
Big data collection and application based on 5G Industrial Internet Three-level edge layer
Authors: Yang Qingfeng , zhang Chonghao , Hu Chunfen , Zhou Haifei , Lou Hua 
Published: December 2020

Abstract
The project is based on 5G+ Industrial Internet to collect the “three-level” edge layer: equipment integration and multi-source heterogeneous data of spare parts manufacturers, equipment manufacturers and equipment users, complete the assimilation of multiple heterogeneous data, establish a unified data standard, and eliminate Discrete enterprise characteristics. Focusing on the informatization of upstream power equipment which is taking into account the informatization of downstream inventory power equipment and realize a large-scale and shallow informatization project of incremental equipment in the Yangtze River Delta in a short period of time, thereby helping companies build general services at the SaaS layer around equipment data. Develop the best process plan to serve enterprise customers in the industry.
Non-fragile infinite time control for networked Lipschitz nonlinear system
Authors: Ma Weiguo , Xu Xia 
Published: December 2020

Abstract
The non-fragile infinite time control is investigated for networked Lipschitz nonlinear system with quantization and incomplete sensor data in this paper. The state of the system is quantized before transmitted to the controller over the network. The Markov Chain is adopted to describe the transmission process. The non-fragile infinite time controller which makes the system stable and performance index minimum is designed in form of linear matrix inequality. A numerical example is given to demonstrate the effectiveness of the proposed method.
Research on sentiment classification of Chinese short text
Authors: Zhu Zhichao , Zhu Zui , Zhu Wenjun 
Published: December 2020

Abstract
Sentiment classification can be widely used in various fields because of the value it has gained, and has received extensive attention in recent years. No matter the traditional methods based on language rules, machine statistical learning, or the current popular deep learning have achieved great success in this respect, but up to now, because of the particularity of Chinese, there are still few methods for Chinese text sentiment classification. Moreover, the traditional method is time-consuming and labor-consuming, with poor versatility. The neural network method saves resources and is generally applicable, but the application of language knowledge is not sufficient. For this reason, we propose a method combining traditional method with neural network, which combines sentiment lexicon, attention mechanism and stacked BILSTM. Word vectors are generated using word2vec, and the context semantics are captured multiple times by stacked BILSTM to get richer semantic information, for sentiment classification, sentiment words often have more influence on the result of classification, so we use sentiment lexicon which contains rich sentiment word to monitor the attention mechanism, and make the attention model pay more attention to sentiment words. Combining the advantages of traditional methods and neural networks, we use the proposed method to do the sentiment binary classification experiments on two data sets. The experimental results show that our proposed method is superior to the previous method.
End-to-End Deep Residual Network for Semantic Segmentation
Authors: Zhou Yingzi , Huang Kun , Guo Xiaoying , He Xiaohai 
Published: December 2020

Abstract
Recent work has made significant progress in improving for the pixelwise labeling with convolutional neural networks by using residual networks. In this paper, we explore the impact of residual network in semantic segmentation by residual encoder-decoder model. The residual block can improves the dimension of feature maps, and obtains more efficient feature maps, and then ensures the effective of deep network structure. Moreover, we select the activat ion function with zero-centered characteristics to speed up the model convergence. The SELU activation function is used to remove the BN function in the network due to normalized deploy ment, thereby reducing the amount of network calculations. The proposed residual encoder-dec oder model can still output high-precision prediction results without any post-processing, and t he convergence speed is significantly faster. Our approach has achieved 61.13% mIoU, 86.92% PA, 73.42% MPA on Camvid dataset.
Dynamic Gesture Recognition Based on iCPM and RNN
Authors: Weihui Chen , Yichao Fan , Ye Zhang 
Published: December 2020

Abstract
Aim to solve the problem that continuous complex actions are difficult to be recognized by computer vision technology in actual production, this paper collects gesture video from single-and double-viewpoint, constructs gesture joint point coordinate recognition network model by improved Convolutional Pose Machine(iCPM) model, obtains gesture Gauss heat map and joint point coordinates in each frame of video, and then inputs joint point coordinates with time series into gesture sequence recognition network. Finally, the gesture sequence is obtained. The experimental results show that the recognition accuracy of gesture recognition model based on double-viewpoint is 4.18% ∼ 6.92% higher than that of the single-viewpoint gesture recognition model, which verifies that the gesture recognition model based on dual-viewpoint has better recognition effect. In addition, the video samples of Tin-Lead Soldering including continuous and complex actions verify that the model has ideal recognition ability for dynamic gesture in actual production process.
Multi-Agent Online Coordination via Bayesian Inverse Planning
Authors: Wang Meiming , Lan Long , Yang Wenjing 
Published: December 2020

Abstract
Multi-agent coordination is a classical applied engineering problem and has been widely used in daily life. To efficiently coordinate all agents in the environment, most traditional models rely on a robust communication system between agents. However, in the scenario of autonomous vehicles, building a reliable communication system is extremely expensive and is very difficult to achieve in the foreseeable future. In this paper, we propose an online multi-agent coordination mechanism for each agent in the absence of communication and explore the benefits of Bayesian inverse planning in achieving the Nash equilibrium which provides an optimal coordination solution for each participant in multi-agent environment. By deliberate design, the proposed Bayesian inverse planning enables each independent agent to infer goals and predict future actions of other agents only by simply observing the past actions of them. To make a timely response to the dynamic environment, the inferences and predictions can be flexibly adjusted accordingly with the continuously updated action information of agents. Based on future action predictions, agents take coordinated actions to achieve Nash equilibrium coordination. We designed several mixed driving scenarios in the real world to test our model, and simulation results show that our model is efficient and flexible, compared with an optimal model and a heuristic model.
The improved mayfly optimization algorithm with Chebyshev map
Authors: Zhao Juan , Gao Zheng-Ming 
Published: December 2020

Abstract
The mayfly optimization (MO) algorithm was just proposed recently, simulation experiments proved that it was capable to optimize both the benchmark functions and the real problems we met. In this paper, the MO algorithm would be improved with Chebyshev map, simulation experiments were carried out and results showed that the improved algorithm would indeed increase the capability.
The improved mayfly optimization algorithm
Authors: Gao Zheng-Ming , Zhao Juan , Li Su-Ruo , Hu Yu-Rong 
Published: December 2020

Abstract
The mayfly optimization (MO) algorithm was proposed with a better hybridization of the particle swarm optimization (PSO) and the differential evolution (DE) algorithms. The velocity would be relevant to the Cartesian distance among the relevant individuals. In this paper, a reasonable revision for the velocity updating equations was proposed based on the idea of moving towards each other as capable as they can. Simulation results proved that the improved MO algorithm would perform better than the original one.
Some Quantum Error-Correcting Codes with d = 5
Authors: Li Husheng , Li Ruihu , Yao Yu , Fu Qiang 
Published: December 2020

Abstract
In this paper, we firstly give a computer-support method of searching for Hermitian self-orthogonal linear code. Then two linear codes with dual distance 5 over field
F

q2
for
q
= 2,3, respectively, are chosen as ingredients. By use of this searching method, we totally find 88 Hermitian self-orthogonal linear codes and construct 88 related quantum error-correcting codes with d = 5 . Most of these results are best known quantum codes and a number of them have better parameters than that of previous quantum codes.
Early forest fire smoke detection based on aerial video
Authors: Nai-Meng Cang , Wan-Jun Yu 
Published: December 2020

Abstract
The early detection of forest fire smoke has more research value than the detection of forest fire flames. This article mainly introduces the content of early forest fire smoke detection based on background blur model and aerial video. First, the background image of the video is extracted by establishing a background model. Then, through wavelet transform, the energy value of the background composite image is compared with the energy value of the target composite image when smoke appears, and the presence of smoke is detected by threshold processing. Experimental results show that the average recognition rate of smoke is as high as 96.94%.
Research on Vision-based Intelligent Detection Method of Circle Features
Authors: Fu Zemin , Wang Zhijin 
Published: December 2020

Abstract
Aiming at the low efficiency and low accuracy of traditional manual detection of workpiece circular features, a machine vision method is proposed to accurately detect circular features. By adopting algorithms such as iterative threshold segmentation, generating XLD contours, selecting feature contours and random Hough transforms, single or multiple circles on the workpiece can be quickly detected. Experiments show that the detection is efficient and accurate, and can meet the detection needs of industry.
A novel image matching algorithm based on PCA and SIFT
Authors: Li Wenju , Na Xinyuan , Su Pan , Lu Yunfan , Dong Tianzhen 
Published: December 2020

Abstract
In order to solve the problem of low matching precision and slow matching speed of image matching algorithm based on classical SIFT, a new image matching algorithm based on PCA, SIFT and improved RANSAC is proposed. Firstly, the SIFT feature was extracted from images; Secondly, principal component analysis is used to reduce the dimension of SIFT feature descriptor, from 128 to 20; then, the EUCLIDEAN distance is used for feature matching; finally, an improved RANSAC algorithm is proposed to eliminate the mismatched feature points. Experimental results show that the proposed algorithm improves the accuracy and speed of image matching.
Research on Digital Operation and Maintenance of Informati on System in Distributed Environment
Authors: Li Qing , Feng Mei , Shen Duanming , Zhang Wenbo 
Published: December 2020

Abstract
In recent years, the idea of information system construction has been continuously innovated due to the micro service architecture and domain driven design. Enterprises also have a new understanding of the importance of their own information security and data assets. But unfortunately, if we continue to use the original operation and maintenance environment and mechanism for waterfall development, the new information system under the micro service mode will not be applicable. For example, due to the lack of operation and maintenance positioning standards brought by the new development framework, the data source is not clear due to the micro service in hundreds of virtual machine environments, and the operation and maintenance quality lacks effective supervision. This requires a set of operation and maintenance standards and system quality assurance applicable to the new micro service design ideas. This research is based on this theory. This system is a collection of activities that give corresponding power and control to the software and hardware management of information. It includes four elements: organization, system, process and tool. The application results in this study show that the construction of this system greatly improves the management efficiency of operation and maintenance at all levels, improves the intelligent level of operation and maintenance, and plays a positive role in promoting the stable operation of the entire information system.
Research and design of activation function hardware implementation methods
Authors: Zhengbo Chen , Lei Tong , Zuoning Chen 
Published: December 2020

Abstract
Activation function is an important part of neural network. Many artificial intelligence accelerators specially design hardware component supporting for activation function. In this article, we analyze normal activation functions, study kinds of activation function hardware implementation methods, and propose a parallel look-up table based piecewise linear fitting method. In many-core AI processor, we determine the table precision by memory size, and propose the basic algorithm and detailed structural design scheme. After the design is completed, we verify its correctness and evaluate its performance. The result shows that proposed method meets the need of correctness, and can finish the parallel computation of sixteen activation function fitting results within thirteen cycles. It greatly improves the efficiency of activation function hardware design.
Estimation of arrival time difference of partial discharge ultrasonic signals based on nonlinear transformation and covariant
Authors: Hu Jinxi , Liu Wenhong , Zhang Haotian , Liu Zhonghua 
Published: December 2020

Abstract
Ultrasonic signals will be generated when high voltage equipment is partially discharged. In order to de-termine the location of partial discharge, the time difference between ultrasonic signal and sensor can be used. In this paper, Alpha stable distribution is used to model noisy ultrasonic signals collected during actual partial discharge, and a nonlinear transformation-covariant time difference estimation method is proposed. Firstly, the signal-to-noise ratio can be improved to a certain extent by preprocessing the noisy signal based on the arctangent nonlinear transformation. Then, the covariant correlation function is used to measure the similarity of the two signals arriving at the sensor, and according to the position of the peak value of the covariant correlation function, the time difference information of signal arrival is obtained. On the basis of theoretical analysis, the experimental results of computer simulation are discussed and summarized. Com-pared with generalized correlation method and covariant algorithm, the proposed method is more suitable for TDOA estimation of PD ultrasonic signals in low SNR and impulsive noise environment.
Research on 5G+MEC Industrial Internet Technology Testing Platform
Authors: Dazhi Jing , Xiaoping Lu , Weijie Zhang , Ming Sun , Yubao Huang , Rui Zhu 
Published: December 2020

Abstract
The 5G+MEC Industrial Internet technology test platform is committed to the application of 5G technology in industrial scenarios. The test platform is supported by 5G+MEC as the technical architecture, and is integrated with machine vision, AR, AGV and other applications; The test platform adopts a modular design. Starting from the test scenarios of the manufacturing process such as interaction, customization, and production, some technologies are transformed into mature solutions through testing, which shortens the application cycle of new technologies and promotes the development of the Industry. Internet.
A Scientometric Review of Automated Journalism: Analysis and Visualization
Authors: Xu Zhichao , Lan Xintong 
Published: December 2020

Abstract
In recent years, automated news has been more and more adopted by media organizations, which has aroused the attention and discussion in the industry and academia. However, few studies have attempted to map out the global research on automated journalism. To analyze the state of research evolution in automated journalism and its trends, this paper aims to analyze the year of publication, most participating countries, institutions, journals, and topics of the relevant papers collected from the Web of Science (WoS) database. The results shows that there are many countries actively participating in automated-journalism-related research, and the USA is the most productive country followed by Germany and Spain. Most of the active journals are in the field of journalism and communication. However, the visualization map of author’s keywords implies the nature of interdisciplinarity of the research, covering the main disciplines of journalism, computer science and automation.