bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and implementation of slotted metamaterial stacked microstrip patch antenna for broadband applications
Authors: Divya C , Koushick V 
Published: January 2020

Abstract
The design of stacked patch antenna integrated with metamaterial for broadband applications to achieve appreciable gain, directivity and low return loss is presented in this article. It mainly focuses on optimization and easy adaptation in MMIC technology. The antenna is designed to carry out by combining two Microstrip patch antenna in a stack manner and Metamaterial is implemented by Complementary Split Ring Resonator (CSRR) technique on both side of the patch antenna and slots are introduced at four corners of the patch and adjacent to the metamaterial. The designed antenna is capable of maintaining its performance over wireless system at C band (4-8 GHz) frequency range. The substrate used is FR-4 with thickness of 0.8mm and permittivity) is 4.2. It is simulated using Advanced Design System (ADS) and it is resonated around 6 GHz. It yields a VSWR of about 1.014 (6.25 GHz), 1.022 (5.829 GHz) and 1.015 (6.522 GHz) under simulation and 1.25 (6.03 GHz), 1.119 (5.74 GHz) and 1.247 (5.36 GHz) in measurements respectively. Thus the antenna becomes a necessity for many applications in recent wireless communications such as RADAR and space applications.
Paradigm shift and challenges in IoT security
Authors: Chopra Ashok 
Published: January 2020

Abstract
All Our world is experiencing major technological changes and this time it is cantered not on internet but Internet of Things. Actually, Internet of Things is sincere effort to connect all unconnected especially smart objects by doing so human wants to challenge their own way of thinking that shouldn’t all objects be connected to one central network and among themselves so that as human we can control them all. So, paradigm is let us provide capability of objects to exhibit the ability to communicate to each other as well to one central system thus to human beings. This effort would certainly bring new improvements and efficiency in our lives. It is this very premise behind Internet of Things (IoT) that would see new transformation and revolution in industrial world from manufacturing, logistics and supply chain in current industrial world of 4.0 and future newer versions. Over last few decades Information Technology (IT) has been witnessing strong and active cyberattacks and creating protection threats to the cyber security networks. Information Technology industry has learnt lot from these threats and have also documented them to find right and appropriate solutions. In contrast Operational Technology environment surrounding Information Technology were kept in abeyance in many organizations. Thus one hardly finds history and documentation of cyberattacks systems built around operational technology. This also makes fewer learning opportunities and record of catalogued incidents and their corresponding risk mitigation strategy not rich in record. It is worth knowing that the world of operational technology addresses wider scope and higher depth in comparison to Information technology. In Operational Technology the word security and safety are synonymous with each other. It is reality that many of the industrial security standard also incorporate equipment, instruments and safety recommendation by experts thus lay the foundation of IoT security. This paper provides a historical perspective of Operational Technology security, the security system measure, how over a period of last few decades the subject matter has evolved, what are few of the real challenges industry is facing. The final phase of paper focuses on some of the real practical measures and steps have been to taken to in creating more safe and secure industrial environment including best practices in creating synergy between Information Technology networks security and old aged legacy industrial environments.
Non-invasive haemoglobin measurement among pregnant women using photoplethysmography and machine learning
Authors: Lakshmi M. , Manimegalai P. , Bhavani S. 
Published: January 2020

AbstractEstimation of haemoglobin(Hb) using non-invasive methods has caught wide interest among researchers all over the globe. Among the various devices and methods adopted for non-invasive haemoglobin (SpHb) measurement, prediction of SpHb using PPG can provide earlier and faster diagnosis. The primary objective and motivation of this study is to assess the efficiency and effectiveness of SpHb monitoring among pregnant women using PPG and generalized linear regression technique. PPG signal was acquired from pregnant women with prior consent and Hb was predicted from the time-domain attributes of PPG. Hb value as calculated by the invasive lab methods was compared with the predicted SpHb value. The absolute bias between the SpHb predicted and Hbrefwas 0.73 g/dL (SD 0.62). To analyse and evaluate the performance of the proposed word, correlation coefficient between SpHb and Hblab, was calculated using IBM SPSS software.
The Application and Improvement Discussion of Gossip in NoSQL Databases: Taking Cassandra as an Analysis Example
Authors: Qian Yichen 
Published: January 2020

Abstract
The application of gossip in NoSQL has a promising future and has significant academic value. By using gossip, a large storage system can be more scalable and stable with better fault-tolerance. This study takes Cassandra as an example to study the advantages of applying the gossip protocol in NoSQL by comparing with the conditions of using other protocols, such as Flooding algorithm and Structured Broadcast algorithm. To improve the efficiency and scalability when the size of individual gossip populations is limited, the author proposes a reconciliation mechanism named weighting. This study reveals that the gossip protocol in Cassandra contributes to linear scalability with limited individual gossip populations by implementing a simulation of gossip transmission rounds.
Verification and analysis of mobile communication network simulation method based on reverse coverage test
Authors: Liu Yunyu , Yu Ming 
Published: January 2020

Abstract
The common mobile communication network simulation method is to use the terminal to measure the downlink data of the base station and carry out the coverage simulation analysis of the mobile communication network. The test workload is large, the labor cost is high, and the accuracy deviation is large.The distributed base station is used to test the uplink data of the terminal, and the wireless link algorithm is used to carry out the coverage simulation analysis of the mobile communication network, so as to collect big data and high precision. Combined with the high-speed distribution processing capacity of the new generation of mobile communication network, the network test, simulation and analysis can be completed.Through verification and comparison, this method has many data sampling points, good real-time performance and high accuracy, and provides technical support for future automatic network planning and intelligent network optimization.
Research on Vehicle Parts Defect Detection Based on Deep Learning
Authors: Liqun Wang , Jiansheng Wu , Dingjin Wu 
Published: January 2020

Abstract
At present, automobiles have become a common means of transportation, but with the increase of vehicles, safety issues have gradually emerged. Therefore, the assembly, manufacture and production of vehicles require systematic testing and rigorous inspection. Therefore, defect detection of vehicle parts is particularly important. Vehicle parts defect detection has evolved from manual detection of traditional classification methods to machine vision methods. In this paper, the deep learning method is used to firstly detect the defects of vehicle parts through the training of VGG16 network structure model. The accuracy rate is 94.36. Secondly, the VGG16 network structure model is improved. By introducing the inceptionv3 module, the width of the model is increased on the basis of depth, the image is better recognized with an accuracy of 95.29. However, the accuracy of the traditional HOG+SVM classification method is only 93.88, and the efficiency of both methods is higher than that of the traditional method.
Application of analogy method in teaching electromagnetic field and electromagnetic wave theory
Authors: Wang Xiu-min 
Published: January 2020

Abstract
Firstly, this paper puts forward an effective analogy teaching method for electromagnetic field and electromagnetic wave theory on the basis of analyzing the teaching characteristics of this course. Then, several aspects of analogy teaching method can be applied in this course are discussed. Finally, the analogy of boundary conditions is taken as an example to illustrate how to use analogy method in practice teaching. It also summarizes several problems that need to be paid attention to when using analogy method in teaching.
Enhanced Adaptive Successive-Cancellation List Decoder
Authors: Jing Ren , Cheng Fengyi 
Published: January 2020

Abstract
Adaptive successive cancellation list decoder (ASCLD) with upsizing scheme as a well-known approach can significantly reduce the average list size of SCL algorithm for polar codes with cyclic redundancy check (CRC). Since it needs to re-decode the whole received bits if no survival path verifies CRC, it dramatically increases the decoding latency. In this paper, a segmented ASCLD is proposed to reduce the decoding delay of ASCLD, which deals with the multi-CRCs concatenated polar codes. Unlike the normal ASCLD, once all the survived paths of one decoding partition cannot pass its corresponding CRC, a new decoding attempt is immediately initiated with larger list size. Moreover, based on analyzing the performance of the proposed system, a series of optimization strategies are used to improve the performance. Simulation results show that the proposed scheme can greatly reduce the delay of upsizing ASCLD without performance loss.
The Method of Collaborative Allocation of Slots and Trajectories Based on Route Resource
Authors: Tian Wen , Yang Fan , Guo Yixing , Song Jinjin 
Published: January 2020

Abstract
In order to solve the problem of cooperative allocation of route resources in the case of congestion or bad weather, this paper proposes a two-stage time slot coordinated allocation model. The two phases will be considered separately from the air traffic control department and the airlines, with the lowest total delay cost of the affected flights and the average flight delay cost between the airlines tend to be the same. In order to verify the model, this paper intends to use the greedy algorithm to solve. The validity of the model is tested by the simulation data of the route, so as to achieve the goal of balancing multiple flow constrained areas and improve the operation efficiency of the route.
Research on Optimization of Industry-University-Research Cooperation Based on Enterprise Incentive
Authors: Chen Lu , Wu Jie , Sheng Yongxiang , Hu Jun , Shi Qinfen 
Published: January 2020

Abstract
Enterprise incentive is an important means to stimulate the enthusiasm of university research and development and promote the innovation and development of enterprise. The paper constructs different game models of industry-university-research cooperation and innovation, and analyzes the stability of the models. The paper also analyzes the cooperation process between enterprise and university and the optimal incentive coefficient of enterprise through the example and numerical simulation of industry-university-research cooperation, and studies the optimization effect of enterprise incentive on industry-university-research cooperation. The conclusion is as follows: enterprise incentive has positive influence on industry-university-research cooperation and innovation, and can improve the efficiency of industry-university-research cooperation and innovation. The size of enterprise incentive has an “inverted u-shaped” influence on the innovation of industry-university-research cooperation, and there is a threshold value.
Design of University Evaluation System based on Big Data
Authors: Wu Ji 
Published: January 2020

Abstract
In the practice of early university evaluation, due to the backwardness of information technology and evaluation methods, the following problems were brought about: the social needs were neglected, the evaluation subject was single, the evaluation method was simple, and the evaluation data source was single. In the paper a big data model of university evaluation is presented, which studies the educational activities from 8 dimensions: core performance, innovation ability, teamwork, resources, audience willingness, parental evaluation, employer evaluation and social contribution. The model takes account of long-term, short-term factors, individual and overall factors, internal and external factors, educators and audience factors, etc., and carries out omnidirectional analysis. Every dimension is analyzed from 2 aspects: positive and negative. Then based on the model, an University Evaluation System based on Big Data (UESBD) was designed and implemented. Finally, the software was used to analyze the data of 20 colleges and universities from multiple dimensions to evaluate the educational activities of education.
The tail risk measurement of bitcoin price fluctuations under strict supervision - based on GPD distribution
Authors: Wang Juan , Tian Feng 
Published: January 2020

Abstract
The research interval is divided into three intervals according to the date of occurrence, “China announced to shut down bitcoin exchanges” and “American Securities and Exchange Commission published the announcement of non-standardization of digital currency exchanges”. And the Generalized Pareto Distribution is used to measure the tail risk of bitcoin price fluctuation in the three intervals. It is found that the Generalized Pareto distribution can better fit the thick tail of the bitcoin yield, and the risk of price fluctuation in the three intervals presents a change of “low-high-low”.
Index System of Fashion Week Vitality Evaluation
Authors: Ding Jiahui , Liu Xiang , Yu Lei , Xi Zhenghao , Yuan Rong 
Published: January 2020

Abstract
The fashion week is a symbol of the fashion world. At the same time, it is the leading international platform for product design, brand influence and fashion promotion. It has the leading role of the fashion industry “wind vane”. In order to accurately measure the development level of the global fashion week, this paper compiled the fashion week vitality evaluation index system through scientific indicator design and detailed objective data. This paper takes fashion data, media data and consumption data as the core, follows the design principles of the index system, combines the characteristics of the fashion week to select indicators, scientifically collects data and adopts the analytic hierarchy process, and finally establishes a scientific fashion week vitality evaluation system.
Current Situation Statistics and Macroeconomic Policy Analysis of China’s Energy Consumption Structure
Authors: Jin Jing , Yu Zipeng 
Published: January 2020

Abstract
With the sustained and rapid development of China’s economy, the energy problem is more prominent. China’s total energy consumption has increased sharply, and its dependence on coal is relatively high. This has also brought serious environmental problems, which will become one of the main reasons restricting China’s economic development. Although the introduction of relevant policies has also achieved certain effects, energy big data is not only the in-depth application of big data technology in the energy field, but also the deep integration of energy production, consumption and related technology revolution and big data concept, which will accelerate the development of the energy industry and Business model innovation. At present, China’s energy supply and demand as a whole are stable, the structure continues to be optimized, and comprehensive analysis gives relevant development suggestions.
Research on Forecast of Beijing Housing Price based on Spatial Gray Markov Model
Authors: Chen Xingxing , Qiu Juliao , Wu Yanke 
Published: January 2020

Abstract
In recent years, with the continuous advancement of the economy, the real estate industry has developed rapidly, which has led to the persistent rise of housing prices. How to understand the changing trend of housing prices reasonably and formulate corresponding policies to effectively control them has become a social problem for the government departments. However, to maintain the housing development, it is critical to monitor and predict housing prices accurately so as to support the decision-making process in the housing field. Based on the actual data of housing prices from January 2018 to March 2019 in Beijing, this research has built a model named Spatial Gray Markov Model, through combing the gray GM (1,1) prediction, and other models, to predict the housing price in Beijing. And the result shows that the Spatial Gray Markov Model is much better than the other models in forecasting, and we get a better outcome that the error and accuracy of the predicted has improved and its accuracy is increasing of 0.31% compared to that of the Grey GM (1, 1) prediction value.
Blockchain-Based Identity Authentication and Intelligent Credit Reporting
Authors: Zhu Xingxiong 
Published: January 2020

Abstract
This paper elaborates on the blockchain-based identity authentication and intelligent credit reporting method. The technologies of multidimensional authentication, multifactor weighted score calculation, security threshold, distributed ledger, smart contract and encryption algorithm are proposed to realize dynamic identity security authentication and intelligent risk control. The user behaviour analysis system, real-time risk identification system and intelligent risk prevention system are constructed. The multidimensional credit data collection is realized through blockchain distributed nodes. The distributed ledger of credit reporting, intelligent pricing of assets and automatic credit rating of users are proposed. It realizes credit reporting system and service based on multidimensional data, distributed ledgers, and smart contracts.
Asymptotic estimates for ruin probabilities of a discrete-time risk model with dependence structures
Authors: Jing Hao-jie , Peng Jiang-yan , Jiang Zhi-quan 
Published: January 2020

Abstract
We study a discrete-time risk model of double dependence structures. The insurance company can make two investments, namely, risk-free and risky investments. The claim sizes follow a dependence structure, we assume that the dependence structure is one-sided linear process in which innovations is independent and identically distributed (i.i.d.), the innovations and financial risks are also form a dependence structure. When the distributions of the innovations belong to the intersection of the dominated-variation class and the class of long-tailed distributions, we derive some asymptotic formulas for the finite-time ruin probability.
Research on game cooperation strategy of supplier and retailer information sharing
Authors: Dai Debao , Shi Yanyan 
Published: January 2020

Abstract
Information sharing between suppliers and retailers is an effective way to improve the overall benefits of the supply chain. This paper uses game theory to construct an evolutionary game model of whether or not suppliers and retailers cooperate in information sharing. The research results show that the key factors affecting the selection of cooperation strategies between the two parties are risk costs and market revenue increments. The proposal for improving the cooperation between the two parties is to strengthen supply chain cooperation, establish a comprehensive information-based supervision platform, and rationally distribute income.
Numerical study on the influence of initial conditions on quasi-periodic oscillation of double pendulum system
Authors: Yao Yupeng 
Published: January 2020

Abstract
The double pendulum system is a common and typical model to investigate nonliner dynamics due to its various and complex dynamical phenomenon. In this paper, the effects of several parameters on the system was studied, such as the length and the initial release angle of the pendulums. Though the Swing length - frequency diagram, the details of ocsillation was researched and a non-smooth inflection point was observed on the curve.
Analysis of the Douban online review of the MCU: based on LDA topic model
Authors: Xu Aiting , Qi Tianxing , Dong Xinzhi 
Published: January 2020

Abstract
With the rapid development of the Internet and the movie industry, online reviews have shown explosive growth. Online review data contains a lot of valuable information, through text mining techniques such as the LDA topic model to analyze this information, to help the movie creators to understand the public’s viewing needs, movie production team to reflect on the shortcomings in the production process, in order to promote the development of the movie industry to provide a reference. In this paper, the Douban online review data of the MCU are used as the research object, and the Chinese text is analyzed based on the LDA topic model. Firstly, data collection and preprocessing are carried out with various software. Then, the word cloud is used to visualize the core information in the online review data, and then the LDA topic model is used for a deeper semantic mining. The research results show that the rich and varied plot design, the audiovisual feast brought by advanced digital technology, the clever connection of bonus scene, the unique characters and actors and a super-hero’s commercial movie mode are closely related to the success of the MCU.
Discrimination of molten pool penetration based on genetic algorithm optimization of BP neural network
Authors: Chang Boxue , Huang Jingyue 
Published: January 2020

Abstract
The metal spatter and light intensity of CO2 welding in the vicinity of the melt pool during the short transition of the melt droplet seriously affect the realtime and reliability of weld feature extraction. The mapping relationship between welding pool characteristic parameters and melting depth is established by using BP neural network optimized by genetic algorithm. The results show that the training results and test results of the optimized BP neural network model of genetic algorithm have little error and meet the requirements of precision. The model can well reflect the relationship between the melting depth and the characteristic parameters of the melting pool.
The Protection of Database Copyright in the Era of Big Data
Authors: Yali Cheng 
Published: January 2020

Abstract
The development of computer technology, mobile intercommunication, cloud computing and Internet of things embraces a limitless prospect for the application of big data. The paper reviews the definition of big data and summarizes the design features by proposing “the Five-Star Feature Model”. The paper then takes the protection of English corpus constructed in China as one special kind of big data to demonstrate the necessity of establishing a comprehensive protection legal system by revealing the setbacks of the Copyright Law in China. With reference to academic literature in China and other regulations in Western countries concerning copyright protection of database, the author finally recommends the complementary use of Law of the PRC against Unfair Competition for the commercial use of these corpora and further proposes to set a new special database right to guarantee the balance of copyright protection and public use of these big data.