bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Influencing Factors of Automotive Body-in-White Laser Welding Quality
Authors: Yue Zhang
Published: May 2020

Laser welding technology is fast, advanced, and flexible. It is the key technology to ensure the development of new automotive products. It is also a technology to improve product quality and reduce costs. However, the application of laser technology in automobile manufacturing still has a certain distance compared with foreign countries. In order to improve the competitiveness of the automotive industry, it is necessary to strengthen the research on the application of laser welding technology in automobile body-in-white. This article will start with the factors that affect the quality of laser welding of automobile body-in-white, and based on the analysis of laser welding mechanism and related characteristics, talk about the influence of laser welding power and give relevant solutions welding speed on the quality of laser welding of automobile body-in-white.
A Study on the Network Characteristics of the Road Collection and Distribution System of Tianjin Port
Authors: Guanhui Wang
Published: May 2020

The competition among ports is changing from the competition in internal collection and distribution system to that in hinterland, in which road network plays a very important role. Focusing on the hinterland road network of Tianjin Port, this study takes it as a network, and analyzes its network topology feature. The conclusion has guiding significance for the optimization of Tianjin Port road network.
A speed guidance strategy based on cooperative vehicle-infrastructure environment at signalized intersections
Authors: Xu Liping , Deng Mingjun
Published: May 2020

It is a problem that most traffic scholars have been studying to enable motor vehicles to pass through signalized intersections without parking or with little parking, so as to improve the traffic efficiency of urban road network and reduce congestion. In the vehicle-road collaboration environment, speed guidance strategy has become an effective solution and trend, aiming at the characteristics and shortcomings of the current trunk signal coordination control as a passive response control based on vehicle arrival drive. In this paper, the principle of speed guidance strategy at signalized intersections is systematically proposed, and the corresponding speed guidance strategy model is proposed according to the different traffic conditions when the guided vehicles arrive at the intersections. This strategy can make the guided vehicles pass through the intersections without stopping or stopping time at least, reduce the delay of vehicles and improve the traffic efficiency at the intersections.
Research and evaluation of one-way traffic setting method
Authors: Zhu Tengzhou , Deng Mingjun , Gong Yongtai , Huang Xinyong
Published: May 2020

As an effective means of traffic organization, one-way traffic plays an important role in easing traffic congestion. However, the reality is often a one-way traffic organization based on experience, and new traffic problems have emerged during the implementation process. Based on the discussion of one-way traffic setting conditions, this paper models and evaluates the one-way traffic organization scheme that meets the conditions. The fuzzy distribution-analytic hierarchy process is used to evaluate the traffic patency, safety, economy, comfort and environmental impact of the organization scheme. Then, the setting condition-evaluation method is applied to the actual case in combination with VISSIM simulation for inspection. Finally, the experimental results show that the setting conditions-evaluation method can provide a scientific basis for the design and comparison process of one-way traffic organization.
Design and Implementation of Vehicle Collision Detection and Alarm System Based on Smartphone
Authors: Yang Ding , Deng Mingjun , Jiang Xueqing
Published: May 2020

Every year, there are a large number of people died in traffic accidents in our country. If people can get help in time after a traffic accident, the survival rate of the people who are treated can be greatly increased. Therefore, it is very necessary to have a system that can detect traffic accidents and call the police or notify the rescuers in time. According to the survey and analysis, the functions of some similar systems in China are not perfect and most of the cost are high, which cannot be widely promoted in China with a high private car base. Based on the acceleration sequence value obtained by the smartphone accelerometer, this project designs the vehicle collision detection algorithm and realizes the collision detection during the driving process of the vehicle in the android environment. When a collision event is detected, the system can promptly send the collision information to the preset phone number and drive the camera function of the mobile phone, perform instant photo forensics on the scene, and finally send the help information and the accident photo to the preset phone number. The project system can realize the detection and alarm function of automobile collision accidents in time, and provide timely rescue after the collision accident.
Research on Cold Chain Logistics Path Optimization Considering Cascading Failure
Authors: Fan Zifu , Ding Huilin , Zhang Wei
Published: May 2020

Considering that the storage and transportation products in cold chain logistics are perishable and deteriorate easily, in the actual distribution process, the cascading failure of urban roads will affect the transit times and distribution costs of delivery vehicles. According to the cascading failure process under the edge failure condition, a new diversion rule is established, and then the distribution cost is associated with the transportation speed. The cold chain logistics distribution path optimization model is established, with minimal distribution costs as its objective function, and a Dijkstra-GA algorithm is developed. The Dijkstra-GA hybrid algorithm solves the model. The model is verified by simulation examples. The simulation results verify the applicability of the model and the algorithm.
Research on Electric Vehicles Considering V2G Technology in Photovoltaic Residential Areas
Authors: Ma Shanting , Su Haifeng , Yu Yibing
Published: May 2020

With the emergence of global energy crisis, electric vehicles and photovoltaic power generation have been widely used as an effective way. However, the connection of large-scale electric vehicles and the uncertainty of photovoltaic power generation also do harm to the operation of the power distribution system. In order to solve the problem that electrical load of resident varies big between peak and valley caused by disordered charging of electric vehicles, this paper constructed a cooperative scheduling model for V2G electric vehicles and photovoltaic power generation. Taking residential community as research background, this paper try to solve the SOC threshold of electric vehicle discharge by reducing the grid load variance and the objective function. The reasonable SOC threshold is optimized by the improved particle swarm optimization algorithm, and the reasonable SOC threshold can be compared and analyzed to reduce the load variance value more effectively. Finally, the paper takes one residential area as an example to prove the rationality and practicality of the proposed model.
Evaluation of Induction Heating Effects on Asphalt Concretes Containing Waste Steel Shavings and Iron-based Particles
Authors: Pang Qi , Zhang Jiangbo , Gao Lei , Sun Bo , Yao Shenglei
Published: May 2020

The self-healing ability of asphalt has great benefits for the service of asphalt pavement, while this ability is not significant at room temperature. This paper utilized waste steel shavings (WSS) and iron-based particles (IBP) as conductor materials, and induction heating technology to heat asphalt concretes. High temperature rutting test, low temperature cracking test and freeze-thaw splitting test were carried out to evaluate the effect of WSS and IBP on the pavement performance of asphalt concretes, and induction heating test was conducted to analyse the heating efficiency. Results shows that both WSS and IBP are beneficial to high temperature performance of asphalt concrete, but not good for low temperature performance. Besides, WSS and IBP has no significant impact on the water stability. Considering the pavement performance and induction heating efficiency, 6%-8% WSS with 2% IBP is recommended.
A Real-time Traffic Sign Detection Model Based on Improved YOLOv3
Authors: Liu XiongFei , Xiong Fan
Published: May 2020

Advanced driver assistance system, which can quickly and accurately detect driver assistance personnel, ensure traffic safety, and play an important role in guiding road traffic signs. However, traditional detection models often have false detection, missed detection, and predicted low accuracy of the target box, especially for the Identification of small targets, its accuracy is difficult to meet the requirements. Therefore, based on the target detection algorithm model YOLOv3, this paper replaces the common two-dimensional convolution with dilated convolution and expands the receptive field, At the end of the model, a three-layer spatial pyramid cascade structure is added to make the multi-scale feature map deep fusion. finally, the improved model is tested for performance on the GTSDB dataset. The results show that the improved model implemented in this paper can effectively solve some problems in the actual road traffic sign detection, and meet the requirements of real-time detection.
Bridge Deterioration Analysis Based on Censored Data: A Case Study in Yunnan Province
Authors: He Xin , Wang Wei , Hu Weidi , Zhang Hanyu
Published: May 2020

Bridge deterioration reflects the change of bridge condition and it is caused by many factors. Predicting bridge deterioration as a research problem has attracted increasing attention around the world. Most methods contrived in previous work and the results are satisfactory. Despite their considerable success, these methods are weighed down by some problems. First, nature environment and climate factors are not considered. Second, they neglected the existence of censored data. Censored data means that the event outcome of interest can’t be observed exactly because of some reasons. It is usual in bridge deterioration prediction and will cause deviation between the prediction result and the actual situation. In this paper, we propose a method to tackle the censored data issue and add nature environment and climate factors into deterioration analysis. This method uses an indicator variable to identify censored data in parameter estimation. In order to ensure the objectiveness of experiment, data collected from the Yunnan Communications Investment & Construction Group LTD are used in this study. Empirical results show that different structure and material of bridge may have different effect on bridge condition. In addition, we confirmed that nature environment and climate can affect the deterioration rate of bridge comparing with other factors in Yunnan Province. The bridges in the areas with moderate temperature and large precipitation may deteriorate more slowly. These findings are important for bridge management and maintenance.
Application of RBF Sliding Mode Control in Pump System for Electric Vehicle Heat Air Conditioning
Authors: Zheng Taixiong , Hou Xiaokang , Yuan Rongdi
Published: May 2020

Aiming at the characteristics of time-varying, time-delay and non-linearity of electric vehicle heat pump air-conditioning system, this paper presents a sliding mode control method based on RBF neural network. The RBF neural network can approximate the nonlinear function with arbitrary precision under certain conditions, and has strong self-learning and self-adaptive ability. Combining the RBF neural network with the sliding mode variable structure control not only has the anti-interference of the sliding mode control and the robustness to the parameter changes, but also effectively reduces the flutter caused by the sliding mode control. The simulation results show that the RBF neural network sliding mode control method can improve the system control accuracy, effectively weaken the chattering, and has good adaptability to the interference conditions.
Research on the application of big data technology in government management
Authors: Chen Mingtsung , He Jiaqi
Published: May 2020

With the advent of the new technology times, big data technology has been widely used as an important strategy of comprehensive social governance. The development of big data not only represents huge amount of data, advanced information technology and huge business opportunities, but also is an effective method to solve problems, transform the world and explore new ideas and scientific applications in the future. However, big data in economic development does not mean that it can replace all the rational thinking of social problems. The logic of scientific development cannot be annihilated in the mass data. In this era of rapid development of intelligent hardware, an important problem for application developers is how to find the delicate balance between power, coverage, transmission rate and cost. Enterprise organizations can use relevant data and analysis to help them reduce costs, improve efficiency, develop new products and make more informed business decisions.
Analysis of microblog public opinion based on big data technology
Authors: Cai Baojing , Li Wensi
Published: May 2020

With the advent of new technology era, big data technology has been widely used in the public communication of mainstream media and become an important strategy. With the continuous progress of the times, big data has entered thousands of households. The arrival of the mainstream media in the new era has changed the traditional public opinion environment, and also brought new challenges to the mainstream media how to deal with the sudden public opinion correctly. Sudden public events are easy to cause public opinion, which is easy to spread, ferment, and even lead to the aggravation of social contradictions. In the field of public communication vision, this paper analyzes the characteristics of public communication of new mainstream media, points out that the new mainstream media face the challenge of news release and public opinion guidance in the current major emergencies, focuses on the analysis of the public opinion guidance effect of people’s daily micro blog in the communication process of Changchun long life vaccine event, and expounds the public communication of the current official public opinion field in new media What should be done in broadcasting. Under the influence of public opinion crisis, how the new mainstream media to use the communication strategy to guide the citizens’ network participation scientifically and correctly, and then solve the social contradictions efficiently has become an important topic.
Research on Personal Data Protection of EU General Data Protection Regulation
Authors: Hu Pengnian , Wei Qian
Published: May 2020

The EU’s General Data Protection Regulation (hereinafter referred to as GDPR), which came into effect on May 25, 2018, is the most concerned personal data protection legal document in the world, and it will inevitably have a profound impact on the construction of personal data protection systems in other countries and regions. It is generally believed that the GDPR is a further implementation and extension of the EU’s consistent position to strengthen the protection of personal data as a basic right.
Simulation Research on Influence of Neural Networks Scale on its Performance
Authors: Wu Xilei
Published: May 2020

When using deep learning methods to deal with specific problems, the structure and configuration complexity of neural networks model have a great impact on the performance of the system. This paper constructed a multi-layer perceptual neural networks model basing on classification problem of film and television reviews. The paper also analyzed the forward and back propagation, processing algorithm and key parameter optimization method, and built the simulation environment. By adjusting the network depth and the number of neurons in each layer, the paper analyzed the influence of the complexity of the neural networks model on the network performance. Then the paper gave the analysis results under the condition of different model scale. Finally the paper purported conclusion in the last section that the performance of the neural networks model changes greatly with number of neurons in each layer changed, but the performance changes little with network depth changed. It can provide an important reference for two classification problems in different fields.
RETRACTED: Problems And Solutions Of China’s Credit Rating Industry Under The Background Of Big Data
Authors: Liu Liang
Published: May 2020

Customer credit rating adopts scientific methods and standardized procedures to investigate, analyze, evaluate, measure and review the ability of rating objects. The objective of the system is to fulfill corresponding economic commitments. Today, big data enriches the data source of credit data that surpasses that of traditional financial data. This paper starts with the concept of enterprise credit rating system, through the understanding of credit rating system and the combination with big data, to understand the existing problems and the causes of the current credit rating system in China.
Discussion of the Risks and Supervision of Financial Big Data Applications
Authors: Ma Liyun
Published: May 2020

With the innovation of big data finance, the existing financial industry and financial institution structure have been affected to a certain extent, and the risk of financial market has undergone a profound change. In the development of Internet finance, big data technology has been recognized and widely used in various industries, financial big data and Internet finance are two important players to transform the competitiveness of the financial industry for Chinese financial ecology. In order to identify the risks in financial markets in the application of financial big data, we need to make clear the characteristics of financial big data. The essence of big data finance is financial innovation. In the process of financial innovation, it not only increases the volatility of the financial system, but also causes the homogenization of financial market. The purpose of this paper is to analyze the risks of big data in finance, to clarify the important matters of financial big data supervision, and to improve the relevant laws and regulations, strengthen the information sharing mechanism and other big data financial supervision measures in view of the existing data monopoly, personal privacy leakage and many other issues, and to provide theoretical reference for the development of big data finance and the ecological stability of financial market in China.
Mathematical Model of Seat Arrangement in Large Gymnasium
Authors: Sun Shu-E
Published: May 2020

In China, where the population ranks first in the world and the total economic output ranks second in the world, the construction of large-scale stadiums or Olympic sports centers has become a trend. When we arrange the seats on the viewing platform, we should not only consider the rationality, practicability and aesthetic value of the seat distribution, but also consider the safety of evacuation.Therefore, under the comprehensive consideration of the above factors, the mathematical model of periodic jump sequence is established, that is, an arithmetic sequence of periodical arithmetic jump, which provides an effective theoretical basis for engineering designers to use computers to process large data and arrange seats scientifically, and also provides an operable accurate model for later programming calculation and simulation technology research.
A Scientometric Analysis of Self-tracking in Relation to Artificial Intelligence and Big Data
Authors: Wu Xue , Zhao Man , Liao Han-Teng , Sun Si-Pan
Published: May 2020

As an increasing amount of personal data has been gathered by wearable and mobile devices, self-tracking, or the practice that people keep track, has become an important topic in Artificial Intelligence (AI) and big data applications. With the aim to provide a systematic review of the literature on self-tracking, this paper presents a scientometric analysis of 109 articles since 2000 collected from the Web of Science. Based on keyword co-occurrence network analysis, the paper has identified four major clusters: (1) wearables as quantified-self applications; (2) big data and critical theory; (3) data and privacy; (4) personal informatics. The further keywords-in-context (KWIC) analysis of the abstracts of the dataset clarifies the seemingly-interchangeable notions of “self-tracking” and “quantified-self”: While “self-tracking” refers to more general activities, practices, technologies, and applications of keeping tracks, “quantified-self” refers to the more conscious efforts and meaning-making outcomes of the self-tracking activities. Such clarification, along with the keyword network analysis, suggests that self-tracking has become a specific and major type of datafication of human conditions or existence and that quantified-self is the construction of self through such datafication. A more integrated conceptual framework is needed for future research to better understand what amounts to meaningful datafication of human conditions and existence, thereby helping researchers and designers to discern the classic notions of health, wellness, and happiness for better research and design outcomes.
Research on short-term traffic flow prediction model based on RNN-LSTM
Authors: Liu Shuying , Li Zhang , Li Hangfu
Published: May 2020

With the rapid development of economy and the increasing number of cars, it is the first task to effectively predict the traffic flow of road traffic to alleviate road congestion and reduce traffic accidents. Aiming at the uncertainty and nonlinearity of traffic flow data, the traffic flow prediction model based on long-term and short-term memory is designed and compared with the traditional BP model and RNN model. The simulation results show that the LSTM model has a higher prediction accuracy and better prediction effect on traffic flow.
Accounting, Auditing, Accountability: Key Insights from a Multidisciplinary Review of AI and Big Data Literature
Authors: Pan Chung-Lien , Pan Yu-Chun , Xu Zhichao , Chen Zizhen , Yu Lin , Qiu Jingjing , Lin Zhuoshan , Li JiaLong
Published: May 2020

Big Data and Artificial Intelligence (AI) projects have been rapidly applied by the diversity of applications, as well as effectively uncover the secret behind the data. In recent years, Big Data and AI have also had a huge impact on accounting and auditing, such as the launch of intelligent robots. This research puts forward a review of literature on Big Data, AI, and Machine Learning with Accounting, Auditing, and Accountability, based on 259 articles retrieved from Web of Science Database since 1989 to 2019. With the database, the author analyzed occurrences and produced the data contact network with the United States as the largest data resource subject and associated in three directions. Through the comparison of the three figures, it is not difficult to find that the countries with advantages in data resources have more influence in an organizational and sources cluster, and the United States is the most obvious example.
A New Quick View Retrieval Method for GF-6 Remote Sensing Images
Authors: Deng Guang , Peng Zhang , Li Zhiyong , Tian Xin
Published: May 2020

As of November 3, 2019, all seven satellites for project of the high-resolution earth observation system have been launched. We have ushered in an era of massive remote sensing data. When we do large-scale forest land mapping, such as national and provincial scale, we need to retrieve images that meet the image quality requirements, are as cloudless as possible, cover the research area, and have as few images as possible. The conventional practice is that the application unit searches the web page provided by the remote sensing data provider for the remote sensing data it needs, and orders to obtain the data. It is often time-consuming and laborious to query data that meet the needs. This paper puts forward a new selection process method of GF-6, GF-1 and GF-2 satellite data. This is a step-by-step method. First, the cloud coverage, time and area are selected according to the metadatas provided by the data provider to meet the requirements. Then, according to the image quality and cloud amount set by the program, it is selected again. Finally, in this image set, the final selected image is given according to the requirements of maximum coverage of the research area and minimum overlap of the image itself. The method can greatly save the time for manually selecting images and improve the working efficiency. This paper designs a program, which can be used conveniently by users. The paper introduces the idea of program design in detail.
A Localization Algorithm Based on NLOS Identification with Improved VB-AKF
Authors: Chu Hao , Fan Zhibo , Wang Fei , Ji Peng , Wu Chengdong
Published: May 2020

As a research hotspot, wireless sensor network (WSN) has a wide range of application prospect. The non-line of sight (NLOS) localization is one of the most basic techniques. In order to mitigate the NLOS influence and increase the localization accuracy, we proposes an improved VB-AKF algorithm in this paper. Firstly, the propagation state of the signal is identified. Then, for the LOS and NLOS mix environments, we reduce the NLOS error by changing the attenuation coefficient. And the maximum likelihood (ML) method is used for localization. Finally, the simulation results show that our method can effectively obtain higher localization accuracy.
Exploring different notions of literacy: a literature review analysis of literacy research related to Artificial Intelligence and Big Data application
Authors: Wang Zijia
Published: May 2020

Artificial Intelligence (AI) and Big Data projects have been rapidly applied by organizations and adopted by younger users, leading to an increasing number of various capacity building applications, as well as significant impacts on the well-being of both designers and users. This research presents a review of literacy-related literature on “Big Data”, “Artificial Intelligence”, “Machine Learning”, based on 73 articles retrieved from the Web of Science Database. The author produced a keyword co-occurrence network map highlighting three main domains: digital literacy, information literacy, and data literacy. This paper concluded that data and information literacy share similar semantic networks whereas digital literacy is related more to pedagogy and innovations, while being the more distant node from both data and information literacy. For stakeholders such as educators, academic researchers, and policymakers, the paper provides reference points for understanding technological literacy and the current relationship among key literacy concepts and domains in relation to AI and Big Data
An enhanced artificial bee colony algorithm for numerical function optimization
Authors: Yu Yi , Wu Yonggang , Liu Xinglong
Published: May 2020

In response to the problems of slow convergence, low convergence accuracy and easy to fall in local optimum of the standard artificial bee colony (ABC) algorithm, this paper proposed the following two improvement measures: Firstly, on the basis of using the current optimal solution, the gradient modification strategy was used to reduce the leading role of the current optimal solution, so that the nectar resources could be fully searched to ensure the diversity of the population; Secondly, the crossover and mutation operation in genetic algorithm was used to replace the random search of scout bees, and the prior information was effectively utilized, which not only improved the search efficiency but also ensured the diversity of population to a certain extent. The numerical experiment results show that the proposed algorithm has good convergence accuracy and robustness, and also presents an excellent performance in multi-dimensional optimization.
A Scientometric Analysis of Artificial Intelligence and Big data for well-being and human potential
Authors: Zhang Zonghe , Liao Han-Teng , Wu Xue , Xu Zhichao
Published: May 2020

Technology has a significant impact on our way of life. Research on artificial intelligence and big data and the cross-field of human well-being plays an important role in guiding the application of science and technology to the people-oriented and good application direction. The reviews in this field focused on the achievements of artificial intelligence and big data in a specific field, such as subjective well-being/biobanks/Translational medicine (TM)/connected health (CH) and medical Internet of Things(mIoT). There is no bibliometric study on the use of artificial intelligence and big data to promote human happiness and health. To provide a systematic review of the literature on Positive Artificial Intelligence and Big data, this paper presents the main institutions, authors, funding agencies and disciplines, based on a scientometric analysis of 243 articles collected from Web of Science.
Research on Virtualization Security in Cloud Computing
Authors: Chen Lei , Xian Ming , Liu Jian , Wang Huimei
Published: May 2020

Cloud computing services, with its advantages of flexible configuration, efficient utilization of resources and easy management, have achieved rapid development and wide application, in recent years. However, the security issues in cloud computing are also becoming increasingly prominent, at same time, cloud computing security has become an important factor hindering the development of cloud computing. The virtualization technology is one of the core technologies of cloud computing, meanwhile, virtualization security is the core issue of cloud computing security as well. This article introduces the mainstream virtualization technology, reviews the security threats of virtualization in cloud computing, and puts forward the corresponding countermeasures.
The Science and Technology Innovation Report on the Ocean Big Data Application along the ice silk road
Authors: Lin Bo
Published: May 2020

Over the past twenty years, the National Marine Environmental Forecasting Center (NMEFC) has carried out safety and security tasks for a total of 9 batches of Arctic science and technology navigations and 22 voyages of merchant ships along the North East Arctic waterway. The paper summarizes their successful experience and technology innovation achievements of applying on big data, focusing on the infrastructure innovation construction of the ocean forecast big data: creating an autonomous Arctic observation system, exploring the Arctic international cooperation model, participating in the global regional observation plan ; scientific and technological business innovation: research method innovation, security technology innovation, super-computing innovation; as well as science innovation achievement: science research achievements, application technology achievements, computer technology achievements. Finally, a brief prospect for the future of the Arctic is given from the perspective of prediction.
Robust Multi-View Feature Selection Method
Authors: Liu Yufang , Xu Sibin , Huang He , Zhao Fei , Wang Linyu , Liang Xin , Tang Ruixin
Published: May 2020

Recently, Multi-view learning has attracted widely attention. Inspired by the idea of RFS [6], we propose to utilize the Multi-View features to develop a Multi-View feature selection method. In our work, the weight of each view can be adaptively adjusted. An efficient algorithm is designed to solve the proposed method. Numerical study confirms the effectiveness of our proposed method.
Research on Development Strategy of News App under the Background of Artificial Intelligence
Authors: Guo Wei , Zhang Bo
Published: May 2020

With the rapid development of the mobile Internet, a large number of Internet users have access to current news through the mobile phone. The market performance of the media Applictipon(referred to as App) is strong, and it has become increasingly prominent in the public opinion. This article analyzes the present situation of the development of media APP by consulting literature, market research, comparative research and other methods, aiming at exploring and studying the future development trend of media APP and its development countermeasures.
Research on Legal Protection System of Personal Information in Big Data Era
Authors: Deng Xianghua , Zhang Bo
Published: May 2020

In the era of big data, the problem of leakage of personal information is becoming more and more serious. With the citizens’ increasing legal awareness, people pay more and more attention to the protection of personal information. In the context of big data, this article is based on the impact of big data on the protection of personal information. By studying the current status of the domestic and international legal protection of personal information, this paper puts forward some proposals for the protection of information, combined with the current status and existing problems of the legal protection of personal information,
The Visualization Analysis of Artificial Intelligence Research in Recent 10 Years in China
Authors: Zhang Wei , Li Liling , Liu Jialiang
Published: May 2020

Based on the research literature of artificial intelligence in 2010-2019 in CNKI database, this paper uses the econometric analysis method and CiteSpace software to draw the knowledge map and chart of literature distribution, authors, institutions, discipline distribution, keywords and other dimensions. The paper analyzes the current situation and process of artificial intelligence research in China in the past decade from the perspective of visualization, and abstracts six research topics of artificial intelligence.
Modelling and Simulation of Facility Planning Problem Based on Improved SLP Method
Authors: Zhang Bo , Wang Xianlin
Published: May 2020

Facility planning plays a critical role in various fields. An excellent facility layout can minimize the losses caused by the connection between facility units in production operations. In this paper, we improved the traditional Systematic Layout Planning (SLP) method, combine it with the multi-objective decision optimization model. The Improved SLP (ISLP) method is described in detail in this paper, including its principles, process of building model, and calculation methods. To make the ISLP more suitable for research and practical problems, we established a mathematical model for it. This optimization model was also applied to the existing facility layout problem to realistically validate the effectiveness of the ISLP and the model. Results on the established model were found first, then the Flexsim simulation software was used to simulate the layout of the facility before and after the improvement. Finally, the results of the experiment were analysed and compared.
Human Posture Recognition Method Based On Skeleton Vector With Depth Sensor
Authors: Wang Jian , Liu Xue Hua
Published: May 2020

This paper proposes a human body gesture recognition method. The method collects bone information of the human body through the Kinect depth sensor, and then uses the direction cosine method for feature extraction.Finally, the feature vector is sent to the BP neural network for training and recognition. In the case of less sample training, the system can still accurately identify the five postures of standing, sitting, leaning forward, leaning backward and underarm. The input is composed of low-dimensional bone information, so the speed and recognition response speed during network training is better than the conventional RGB image recognition method. Experiments show that the recognition rate and real-time performance of the system can meet the needs of practical applications.
Exploring research fronts and topics of Big Data and Artificial Intelligence application for cultural heritage and museum research
Authors: Zhao Man , Wu Xue , Liao Han-Teng , Liu Yu
Published: May 2020

Modern technologies have supported the preservation, curation and reuse of human cultural heritage assets and resources, the latest development in Artificial Intelligence (AI) and Big Data technologies are expected to have great potentials in the datafication, digitization, and reuse of such resources. With the aim to reveal the research fronts and topics of such potential development, the paper conducted a bibliometric analysis based on 206 related articles from the Web of Science (WoS) database. The paper presents the findings on publication trends, main publication outlets and disciplines, main keyword clusters and research gaps. The paper also finds that the current research scope of AI and Big Data in cultural heritage and museum applications is still in the early stage of storage, calculation and presentation of Big Data. The paper help researchers, funding agencies and practitioners to understand the current status of AI and Big Data applications in cultural heritage and museum.
Deep Metric Learning for text data based on Triplet Network
Authors: Deng Xin , Wu Wei , Wang Fei
Published: May 2020

Text data has not only the characteristics of high dimension and serialization, but also most of the data are not labeled. This makes it difficult for the traditional method to achieve good results only relying on the training class mark to solve the problem of text classification. Later, some people put forward the method of metric learning to solve the problem of text classification. Its principle is to learn the measurement distance function for a specific task according to different tasks, that is to say, samples are classified by “distance measurement”. The triplet network used in this paper is an algorithm based on “distance measurement”. By optimizing the original triplet network, we can learn a non-linear measurement space with stronger data representation ability, so as to achieve better results than the traditional metric learning. Finally, we analyze the performance and efficiency of the method on IMDB movie review data set, and prove the superiority of the method for text classification.
External Sorting Algorithm: State-of-the-Art and Future Directions
Authors: Chen Wenhan , Liu Yang , Chen Zhiguang , Liu Fang , Xiao Nong
Published: May 2020

The advent of the era of big data provides new opportunities and more challenges to sorting algorithms. The traditional internal sorting algorithm cannot adapt to the explosive growth of data, and the memory cannot accommodate all the data for sorting, so the external sorting algorithm arises at the historic moment. Because of the different application scenarios, storage devices and improvement strategies, there are many kinds of external sorting algorithms. Traditional main memory architecture based on DRAM faces the problems of capacity, energy consumption and reliability. Emerging nonvolatile memory technologies are non-volatile, high-density, byte-addressable, low-power, so they can replace persistent storage, main memory or storage class memory. Though NVM devices provide new choices to the revolution of traditional memory and storage system, traditional external sorting algorithms cannot achieve its performance. This paper first sorts out the development of external sorting algorithm, and summarizes it into four kinds of external sorting algorithm based on HDD, embedded device, SSD and NVM. In addition, the classical external sorting algorithms based on different storage devices are listed, and our opinions are put forward. Finally, this paper proposes three problems that need to be solved urgently in the future development of external
Parameter adaptive LS-SVR based on multi-stages division and cluster sampling for remaining useful life prediction
Authors: Yan Jian-wen , Zhong Xiao-hu , Guo San-min , Fan Yu , Zhang Qiang
Published: May 2020

Remaining useful life (RUL) prediction is an advanced technology to manage life cycles of equipment and to reduce maintenance cost. Support vector regressive (SVR) is one of the frequently-used data driven method in the field of prognostics, and is much suitable for analyzing small samples and multi-dimensional data. However, there is a limitation of traditional SVR model that the linearity will increase as the training data increase. In order to overcome it, this paper proposes the parameter adaptive least square support vector regressive (LS-SVR) based on multi-stages division and cluster sampling. It divides the degradation process of the equipment into several stages according to the value of degradation data and selects the model parameters stage by stage. The optimal model parameters of each stage are calculated through the novel testing data, which are obtained through appending the cluster sampling data to the original testing data. Therefore, the optimal model parameters can be selected adaptively online as the degradation evolves. Finally, the proposed method is verified systematically by a simulated dataset of fatigue crack growth and a real-world degradation dataset of GaAs-based lasers. It is shown that the proposed method is of better effectiveness, reliability and robustness.
Palm-vein verification based on U-Net
Authors: Wang Peng , Qin Huafeng
Published: May 2020

As one of the biometric characteristics, palm-vein have received more and more attention in recent years. However, in practical applications, the vein image capturing is affected by various factors, so many low quality images are produced in recognition system. Generally, the vein networks extracted from such low quality images contains many noises, which degrades the recognition accuracy. To address these problems, this paper proposes an end-to-end convolutional neural network to extract vein feature for verification. Firstly, we label the palm-vein pixel by the combination of some handcraft-based palm-vein image segmentation methods and build a training dataset. Secondly, a U-Net network is trained based on the resulting dataset and its outputs are the probability of pixels to belong to vein pattern. Thirdly, we propose a scheme to encode the outputs of U-Net to obtain the vein network patterns. The experiment results on the public CASIA palm-vein dataset implies the effectiveness of our proposed method.
Thinking on the construction of big data service for public security in megacities -- a case study of Chongqing
Authors: Liu Ge
Published: May 2020

According to the national outline of development of Big Data, combined with the current situation of big data development in Chongqing and the action plan of urban development in Chongqing, this paper, from the perspective of Chongqing public security, analyses the social risks, subjective and objective conditions related on big data, puts forward the Big Data construction goal of Chongqing Municipality public security, and gives the construction and development strategies and suggestions for the implementation of the Big Data project.
Deep Feature Attention and Aggregation for Real-Time Head Semantic Segmentation
Authors: Zhang Zhiqin , Zhang Bo
Published: May 2020

This paper proposes a mobile CNN(Convolutional Neural Networks) architecture named FAANet(Feature Attention Aggregation Net), which focused on self-attention and feature aggregation for real time head semantic segmentation using limited computation resource. The proposed network is based on a single lightweight backbone cnn network and aggregates discriminative features through multiple different scale feature maps, and the different feature maps are fused using self-attention sub-block. Using the proposed multi-scale feature communication and propagation, FAANet substantially reduces the size of model, but still achieves big receptive field and strengthen the generalization ability, which achieves a balance between the computation cost and performance. Experiments on head segmentation dataset demonstrate the improved performance of FAANet with 10 times less FLOPs and 3times faster than the existing state-of-the-art fast semantic segmentation methods and the proposed method can achieve comparable accuracy. Specifically, when computed on one NVIDIA Titan X card, it can achieve 98.3% Mean IOU on our mixed test dataset with only 1.2 GFLOPs and 100 FPS while inferring on a 1024*1024 input image.
Rule Extraction of Generalized Incomplete Variable Multi-granularity Rough Set
Authors: Mo Jing-Lan
Published: May 2020

According to the advantages and disadvantages of feature relation and multigranularity decision rough set in generalized incomplete information system, a multi-granularity rough set model based on feature relation is proposed. Firstly, a variable multi-granularity rough set model is proposed, and an optimistic and pessimistic variable multi-granularity decision rough model based on feature relation is constructed. By combining variable multi-granularity and approximate distribution reduction, the variable multi-granularity approximate distribution reduction is proposed, and all the decision rules and possible decision rules are obtained. The results show that the method of variable multi-granularity approximate distribution reduction and rule acquisition is more reasonable and effective than the original method of single granularity approximate distribution reduction and decision acquisition.
Analysis of Tourism City Image Based on Big Data of Network Text—Take Guangzhou as an Example
Authors: Yang Sen , Zhang Ling , Xu Liping
Published: May 2020

Based on 16979 tourist reviews of six scenic spots in Guangzhou by ctrip.com, using the triple process of open coding, spindle coding and selective coding based on grounded theory, it is found that the tourist image of Guangzhou is mainly composed of cognitive image, emotional image and conative image; the tourists are satisfied with a high degree of willingness to revisit and recommend to their relatives and friends; the tourists are emotional towards Guangzhou tourism High evaluation and strong willingness of tourists to travel.
Vehicle-specific retrieval based on aerial images
Authors: Yang Guoliang , Tang Zhigang , Huang Jianchen
Published: May 2020

UAVs(Unmanned Aerial Vehicle) are currently widely used in civil and military fields. Collecting ground information through UAVs is their main application direction. This paper proposes a vehicle retrieval system based on aerial images, which can retrieve the corresponding target vehicle according to the set conditions. For the retrieval algorithm, we optimized its feature extraction network based on YOLO v3, and optimized it as a two-way residual based on the original residual module to improve its ability to detect small targets. Considering that the position deviation of the prediction frame in the actual detection will greatly affect the detection result, the GIoU position calculation index is adopted to improve the network’s sensitivity to the position information. At the same time, retrieval conditions such as vehicle type and color are added on the basis of detection, so that the model can complete the retrieval of specific vehicle targets. The test results of the finally optimized network on the VEDAI dataset reached 91.2%mAP.
Classification of Diabetic Retinopathy Using Adversarial Training
Authors: Wu Dawen , Liu Shishi , Ban Jian
Published: May 2020

In recent years, the classification of medical images has become more and more important. With the help of the deep learning approach, the classification accuracy of diabetic retinopathy has been greatly improved, and it has brought great benefits to the residents living in the suburbs. However, the researchers found that these neural network models became extremely vulnerable when confronted with an adversarial example. Specifically, some minor changes to the samples can fail the model immediately. In this paper, we use adversarial training methods instead of traditional training methods to improve the model robustness against adversarial example. Experiments showed that using this method in APTOS data set the adversarial accuracy increased from 43% to 83%.