bluuebunny/crossref_metadata_2025_split huggingface dataset:
A new series of moduli components of rank-2 semistable sheaves on

with singularities of mixed dimension
Authors: Ivanov A. N. 
Published: April 2020

Abstract
We construct a new infinite series of irreducible components of the Gieseker-Maruyama moduli scheme

,

, of semistable rank-2 sheaves on

with Chern classes

,

and

, whose general points are sheaves with singularities of mixed dimension. These sheaves are constructed by elementary transformations of stable and properly

-semistable reflexive sheaves along disjoint unions of collections of points and smooth irreducible curves which are rational or complete intersection curves in

. As a special member of this series, we obtain a new component of

.
Bibliography: 12 titles.
Continuity and change in the aspect systems of Vedic and Latin
Authors: Dahl Eystein 
Published: April 2020

AbstractThis paper examines the development of the aspect systems in the Indo-European languages Vedic and Latin. Even though aspectual distinctions are central in the verbal systems of both of these languages at the beginning of their attested traditions, they undergo quite different developments in the course of their history. The Vedic verbal system instantiates a classic case of the development from aspect to tense, whereas Latin maintains an aspect-based verbal system, which survives in the Romance languages. The paper explores the semantic properties of the Vedic and Latin past tenses in some detail from two distinct perspectives: a neo-Reichenbachian model, where aspect is regarded as a type of relation between reference time and event time (cf. e.g.,Dahl 2010,2015), and a model where aspect involves different types of partitive operators (cf.Altshuler 2013,2014). Although these two approaches may first appear to be in conflict, the paper attempts to show that they represent complementary perspectives highlighting different dimensions of aspectual meaning.
Historical change in the Japanese tense-aspect system
Authors: Narrog Heiko 
Published: April 2020

AbstractModern Japanese has a formally very simple tense-aspect system, which at its core has only three forms that are complemented by a number of peripheral markers and constructions. The core of the tense-aspect system was much more elaborate and complex in Classical Japanese. This paper discusses the systems of Modern and Classical Japanese, and then sketches the development from the latter to the former. This development involves the grammaticalization from aspect to tense, the recruitment of lexical means and constructions to renew aspectual categories, and category climbing. Two major paths of grammaticalization can be distinguished.
Analysis of the Informatization Construction of Library
Authors: Chen Liping 
Published: April 2020

Abstract
In the era of the Internet, information technology has been widely used in various fields, to a large extent, it has greatly improved the quality and efficiency of work in various fields. Similarly, it is inevitable to develop towards the direction of informatization construction in the library management. The following is an in-depth analysis of the informatization construction of library management in the Internet era, hoping to effectively improve the quality and promote the long-term development of libraries.
Research on Mixed Learning Mode Based on MOOCs and Micro Class in Internet Background
Authors: Jiaoqingliang  , Sudan  
Published: April 2020

Abstract
In the era of education information and globalization, a new mode of teaching and micro class has emerged in the background of the Internet, which brings new challenges and opportunities to the teaching of the classroom. MOOC has been piloted and applied in many universities in the form of SPOC. As a new form of curriculum, micro-course has been applied to the teaching and learning process. The integration of Moor and micro class resources helps to turn the classroom into a mixed mode. This article will focus on this hot topic to analyse the characteristics of the class, the characteristics of the micro class and the influence on the students and teachers, to improve the quality of teaching and to realize the individualized and active study of the students. The article summarizes the results of blended teaching mode at home and abroad, and explores the development and application of MOOC and micro class resources.
Dynamic Service Composition in Semantic Internet of Things Based on QoS
Authors: Weiwei Zhang , Chengcheng Wang , Sa Zhang , Wanlu Zhu 
Published: April 2020

Abstract
There are some problems in the semantic internet of things, such as the limited resources of atomic services provided by physical devices and the dynamic changes of their environment. Based on this, this paper proposes a dynamic service composition method based on QoS in the semantic Internet of things. According to the QoS ontology of adding context, this method dynamically annotates the services in the semantic Internet of things, and dynamically selects and combines the service set to be selected. It can dynamically provide accurate services for service requesters according to service objects and service environment.
Analysis of technology evolution in 3D printing industry segmentation based on dynamic patent network
Authors: Li Shugang , Zheng Lingling 
Published: April 2020

Abstract
Technological evolution analysis is an important way to analyse the development of industry and the trend of technological change, which has important guiding significance for the government and enterprises to make the technological strategies. The patent network method can effectively identify the key technology of the industry, but it has some shortcomings in judging the technology group and other details. Therefore, this study proposes an identification model of the technology theme clustering based on IPC, which is helpful to fully reveal the evolution characteristics of the industry. This study further introduces the time dimension to build a patent network with multiple attribute nodes based on the patent of the 3D printing industry.
A Scalable Attention Mechanism Based Neural Network for Text Classification
Authors: Zheng Jianyun , Pang Jianmin , Zhang Xiaochuan , Sun Di , Zhou Xin , Zhang Kai , Wang Dong , Li MingLiang , Wang Jun 
Published: April 2020

Abstract
In general, deep learning based text classification methods are considered to be effective but tend to be relatively slow especially for model training. In this work, we present a powerful, so-called “scalable attention mechanism”, which performs better than conventional attention mechanism in terms of both effectiveness and the speed of model training. Based on the scalable attention mechanism, we propose a neural network for text classification. The experimental results on eight representative datasets show that our method can obtain similar accuracy to state-of-the-art methods with training in less than 4 minutes on an NVIDIA GTX 1080Ti GPU. To the best of our knowledge, our method is at least twice faster than all the published deep learning classifiers.
Research on Application Strategy of Deep Learning of Internet of Things Based on Edge Computing Optimization Method
Authors: Wu Huanyu , Guo Yating , Zhao Jingcheng 
Published: April 2020

Abstract
Due to the limitations of network performance IoT deep learning model, in order to optimize the performance of IoT network, this paper proposes an application strategy of IoT deep learning based on edge computing optimization method. Based on the multi-layered structure of learning, the edge calculation method is optimized, and the reduced intermediate data is uploaded at the edge node, thereby reducing the network traffic from the IoT device to the cloud server. On the basis of considering the limitation of edge node service capability in the edge computing process, the optimal strategy of task offload scheduling is formulated to improve the performance of the IoT deep learning model based on edge computing. The experimental results show that the IoT deep learning application strategy based on the edge computing optimization method can efficiently execute multiple deep learning tasks in the edge computing environment, which is superior to other algorithms.
Pooling Method On PCNN in Convolutional Neural Network
Authors: Yao Li-sha , Xu Guo-ming , Zhao Feng 
Published: April 2020

Abstract
The pooling method aggregates the points in the neighborhood in Convolutional Neural Network(CNN). It can not only reduce the dimension, but also improve the results, so that the results are not easy to over-fit. However, the common pooling methods have the problems of single feature and lack of self-adaptability. In order to solve this problem, the Pulse Coupled Neural Network (PCNN) is introduced and a pooling method based on PCNN is proposed. The algorithm learns the weights of each eigenvalue from the convoluted neighborhood sub-region by PCNN and fuses them to get the final pooling result. The experimental results on image recognition datasets MNIST, CIFAR-100 show that the proposed PCNN-based pooling method has better recognition effect and improves the performance of CNN compared with the existing pooling methods.
Research on Multimodal Transport Development in China under the Background of Internet Plus
Authors: Wenwen Jiao , Dongli Wei , Cui Cai 
Published: April 2020

Abstract
By brief introduction of the importance of multimodal transport, which has been paid great attention by Chinese government, this paper reviews the literature and sums up the present situation of China`s multimodal transport. Then, the evolutionary background ofinternet plus logistics in China has been summarized, and the influence on intermodal transport has been analysed. By critically analysing the problems, this paper finally gives some suggestions to solve problems from hardware side and software side.
Data model for smart electricity meter comprehensive verification based on BP neural network
Authors: Wang Qing , Dai Yanjie , Wu You , Liu Yanxi , Wang Tingting , Dong Xianguang 
Published: April 2020

Abstract
This paper proposes a BP neural network based model for smart electricity meter (SEM)comprehensive verification under variation conditions of temperature and humidity. Two-neuron BP neural network model and one-neuron BP neural network model are established, respectively. Theoretical results show that when temperature is fixed, the comprehensive verification error is rarely affected by humidity, whereas when humidity is fixed, the SEM comprehensive verification error has obvious variation with temperature which tends to linear correlation.
Modelling of 330MW Circulating Fluidized Bed Boiler System Based on Deep Belief Network
Authors: Xiong Zhonghao , Lin Yiwei , Li Xin , Yang Jian 
Published: April 2020

Abstract
Circulating Fluidized Bed Boiler (CFBB) is an important part of thermal power plants. The plant’s control system is a complex multivariable system with severe nonlinearities, uncertainty and strong coupling. It is not accurate to use traditional analysis methods to build mathematical models of the system. This paper introduces the modelling method of 330MW circulating fluidized bed boiler coordinated control system based on deep belief network (DBN), using the in-situ data of Inner Mongolia Jinghai Power Plant. Compared with the BP neural network modelling method, the effectiveness of the DBN method in the modelling of 330MW circulating fluidized coordinated control system is proved.
Research on Intelligent Measurement and Control System for Internet of Things in Greenhouse
Authors: Zhang Zhifang 
Published: April 2020

Abstract
With the continuous improvement of the level of agricultural informatization, China’s greenhouse intelligent control system has gained many new development opportunities in technology and scale. However, compared with developed countries, China’s greenhouse intelligent control technology is not stable. Based on past work experience, this paper summarizes the characteristics of intelligent measurement and control system for IoT greenhouses, and introduces the construction method of intelligent measurement and control system for greenhouse IOT through the actual design situation and content.
Design of UHF Tag Antenna Based on Internet of Things
Authors: Huajuan Cao 
Published: April 2020

Abstract
This paper presents a detailed study of UHF RFID system on tag antenna design and performance measure methods based on antenna design knowledge and electromagnetic theory. The paper analyzes the characteristics of UHF tag antenna. The paper provides the compact UHF antenna that covers 840MHz∼965MHz. The impedance of the antenna is easy to adjust. The tag antenna has a good performance which satisfies the design requirements. The paper studies the effect of the metallic ground on the tag antenna performance and designs a low-profile anti-metal UHF tag antenna. An antenna with a pair of F-slots is proposed. The antenna can achieve the good complex impedance matching between the antenna and chip. The simulation results show the anti-metal antenna has good working bandwidth from 840MHz to 965MHz and return loss S11<-10dB.
Search method for weld area in water-cooled wall pipe based on YOLO detection network
Authors: Xianbiao Yang , Yu Wan , Xubi Liu , Shutao Wang , Qiucheng Shen , Chengshuai Qin , Zhenyu Sun , Lihui Wang 
Published: April 2020

Abstract
Focused on the defects that the traditional target detection algorithm has low detection precision and is greatly affected by the industrial imaging environment, YOLO algorithm of computer vision is proposed, the features of the image through the deep convolutional neural network are extracted, and the automatic detection function of the weld area is realized. Firstly, the detected image is preprocessed, and the image inversion, k-nearest median filtering, CLAHE image enhancement, gamma image correction and other algorithms are used to improve the contrast of the weld area in the whole picture. Secondly, the data enhancement algorithm is used to increase the number of training samples and diversity. Finally, the YOLO target detection network is trained by using the training samples obtained from the data enhancement, and the performance of the network is evaluated through test samples. In experiments, 500 images of 50 ray-detected image data are used to train the YOLO target detection network, and the test results are tested in a test set consisting of 50 additional images. The experimental results demonstrate that the accuracy of weld inspection is 96%, it is much better than the accuracy of traditional target detection algorithms, and has guiding value for industrial application.
A Hardware Accelerator Based on Neural Network for Object Detection
Authors: Zhao Tong , Qiao Lufeng , Chen Qinghua , Zhang Qingsong , Li Na 
Published: April 2020

Abstract
At present, deep learning algorithms such as neural networks are widely used in all aspects of artificial intelligence. The computing performance of the CPU is low, and the power consumption of the GPU is large. This topic uses FPGA to study and implement the target detection algorithm. The proposed related technology can meet the accuracy requirements of the unmanned system for target detection, as well as the real-time requirements of the video stream. The frame rate of this design is 23.1FPS, and the mAP value is basically the same as that of CPU and GPU. The delay is 47.2% of Intel CPU, and the energy efficiency is 26% of Titan X GPU.
Message Queue Optimization Model Based on Periodic Execution and Category Priority
Authors: Li Kunying , Li Xiaolian , Qiao Dexin , Ding Yu , Wang Liling 
Published: April 2020

Abstract
In recent years, the state has invested tremendous energy in the development of enterprise informatization construction and has also derived numerous high-quality enterprise informatization forms. At the same time, when the enterprise develops to a certain extent, the demand for change management, innovation mode and strategic transformation of information technology is gradually highlighted. Among them, the rapid development of information technology brings about many business systems, weak structure, difficult to adapt to system integration, information sharing and business collaboration. It is also the next important goal of enterprise information construction solutions. In order to solve the huge throughput problem caused by the integration platform of large enterprises in the face of tens of business system information data integration, the aim is to achieve three kinds of requirements for data integration, business integration and process integration of upstream business systems. Based on execution in the period range, this paper proposes a double-execution optimization algorithm for message queue with periodic execution and priority definition. The algorithm is implemented by analysing the operation behaviour of user roles, systems and time, and combining with the defined message importance tag. The experimental results show that this method can effectively filter, sort and execute a large number of message throughput accurately in a distributed working environment, and reduce the performance and delay risk caused by the complexity of information in the integrated system.
The Development Trend of Artificial Intelligence in Cyberspace Security: A Brief Survey
Authors: Li Ziyuan , Xian Ming , Liu Jian , Wang Huimei 
Published: April 2020

Abstract
In recent years, artificial intelligence and network security have attracted much attention. After decades of practice, artificial intelligence and relevant theoretical methods have provided new ideas for the traditional field of cyberspace security. Knowledge mapping is a tool to show the relationship between the development process and structure of science and technology. At present, there is no knowledge mapping that can describe the development trend of artificial intelligence in the field of cyberspace security. So we used Bibliometric, Citespace and VOSviewer to analyze more than 1, 000 quality articles downloaded from Web of Science (WoS), include literature quantity analysis, co-citation analysis, keyword cooccurrence analysis and Research hotspot analysis.
Application of Autonomous Monitoring Method Based on Distributed Environment Deployment in Network Fault
Authors: Li Xiaolian , Li Kunying , Ding Yu , Wei Daiming , Ma Xianying 
Published: April 2020

Abstract
With the rapid development of information technology and the increasing demand for computing, the scale of cloud environment and distributed deployment is becoming larger and larger. Often, due to the outage of a node, a series of chain reaction problems occur, which makes the production environment suffer unpredictable losses. All kinds of uncertainties, randomness, concurrency and diversity exist in the above-mentioned problems. Because of these factors, it brings great troubles to the relevant staff to locate the causes of network failure. In order to solve the problem of network failure in large-scale data network, it is difficult to locate the fault location accurately, and how to give users accurate feedback on the causes of network failure. In this paper, we construct a random forest algorithm, which is based on agent distributed in each device. The system is trained continuously by machine learning, and the samples are classified by decision tree classifier. Each decision tree represents a judgment of the cause of network failure. When an error occurs, the agent collects and pre-processes the device data information according to the algorithm and feeds it back to the controller for aggregation. Then, the analyzer is used to match, judge and find out the corresponding network faults that meet the requirements. The practical application results show that the design model is feasible, which significantly improves the efficiency of problem tracking and the accuracy of problem feedback screening, but also provides strong support for the follow-up intelligent operation and maintenance.
Real-time Vehicle Pose Measurement Technology based on Multi-sensor Information Fusion
Authors: Zhu Changjian , Zhou Lin , Li Wen , Bi Qiushi , Yang Ruipeng 
Published: April 2020

Abstract
To achieve vehicle unmanned driving, it is necessary to obtain the exact position and posture of the vehicle in real time. To get accuracy position and attitude information, multiple different sensors will be used.However, it’s difficult to deal with the collected information and to integrate the relevant data.This article first introduces the method of obtaining measurement data when using encoder, inertial navigation system and GPS alone.Secondly, using the INS/GPS loose combination system to design a multi-sensor information fusion method.In order to verify the accuracy of the method, a multi-body dynamics simulation model was established and a test bench was built.The simulation and experimental results show that the proposed method can well fuse multi-sensor data and accurately obtain the real-time position and attitude of the vehicle body.
A BIA-NOMA Scheme for Downlink of Two-Cell SISO Network
Authors: Chen Kaiping , Zhou Qingfeng , Wang Juan , Wu Jianjian 
Published: April 2020

Abstract
This paper studies a transmission problem based on a two-cell downlink SISO network. We propose a blind interference alignment transmission scheme based on TDMA by using interference alignment technology, which can improve the DoF of users. In order to improve the average achievable sum-rate of the system, we propose the BIA-NOMA scheme by combining NOMA with BIA technology. Theoretical analysis shows that the BIA-NOMA scheme can effectively improve the achievable rate of the center users, and its sum-rate is higher than that of the BIA-TDMA scheme. At the end of this paper, we give the simulation results to verify the performance of the scheme.
Research on Trust-Based Secure Routing in Wireless Sensor Networks
Authors: He Junyao , Xu Feng 
Published: April 2020

Abstract
With the increasing scale of wireless sensor networks, the limited resource of sensor nodes and the uncertainty of the layout environment, sensor nodes are very vulnerable to malicious attacks, but the traditional security mechanism is not suitable for wireless sensor networks. This paper summarizes the common attack forms, security technologies and the security mechanisms of trust-based routing protocols in wireless sensor networks proposed in recent years, then analyzes and compares these secure routes.
Application research of mixed teaching mode based on Wechat applet
Authors: He Fen 
Published: April 2020

Abstract
in the era of rapid development of mobile Internet, Wechat applet because it does not need to install and uninstall characteristics, become a very popular application. Wechat applet applied to teaching is a new teaching auxiliary means. Firstly, the feasibility of WeChat is analyzed from the perspective of function and students’ willingness to use it. Then it discusses the application strategy of Wechat applet in the course of database application technology teaching. Finally, through the analysis of the effect of Wechat applet auxiliary course teaching, it is shown that Wechat applet is an effective auxiliary means of classroom teaching, which can be promoted and applied in other courses.
RFID network scheduling using an improved bat algorithm
Authors: Lyu Shilei , Wei Zhiwei , Li Zhen 
Published: April 2020

Abstract
The performance of RFID networks can be optimized by RFID reader scheduling. This paper proposed a novel approach using an improved bat algorithm (IBA), which can optimize RFID networks. The IBA algorithm includes 2 improved mechanisms. In the proposed approach, all RFID readers are scheduled to work in appropriate sequence, which can greatly reduce RFID collisions. Experiments on two different RFID networks have been carried out to evaluate the effectiveness. Simulation results show that the proposed approach using IBA has better optimization precision than control algorithms.