bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Capacity Analysis of the Orthogonal Frequency Hopping Stacked Nets in the Data Link
Authors: Chen Juan , Zheng Xueqiang , Zhang Chuankun , Xu Zhengqin , Li Aijing 
Published: September 2019

Abstract
The Link-16 can support stacked nets mode for the Joint Tactical Information Distribution System (JTIDS) uses orthogonal frequency hopping networking. The Link-16 can support 127 stacked nets in theory. The number of stacked nets is restricted to 20 in the United States Navy. There are few studies about the impact of the number of stacked nets to the performance. The number of stacked nets has important aspects to network design and planning of data link. Through analysing the architecture of stacked nets, the mathematical model about frequency conflict between sub-networks is established. Under different slot structure, the influences of average delivery are simulated. The simulation results can support the network programming of data link.
Construction and Security Measurement of Cybersecurity Metrics Framework Based on Network Behavior
Authors: Zhao Xiaolin , Zhao Jingjing , Jiang Xiaoyi , Zhang Xiaoyu , Zhang Wentai 
Published: September 2019

Abstract
Cyberspace security involves national security, enterprise security, personal privacy security and so on, which has been paid more and more attention by the state. Cyberspace security measurement is an important step to protect any network. The important premise is to establish a reasonable and universal security measurement framework for network systems. But the network is very complex, and the network security state is changing all the time. Existing information security models lack the description of network behavior. Aiming at this problem, in this paper, the existing information security models at home and abroad are compared and analysed, and their advantages and disadvantages are summarized. Based on PDR (Protection, Detection, Response) model, this paper adds management functions, and optimizes the original concept of PDR model. On this basis, we add characteristic measurement, efficiency measurement and impact measurement to form 12 indicators to dynamically reveal the evolution mechanism of security characteristics measurement indicators. AHP is used to distribute the weight of each dimension, and the network is evaluated quantitatively from four dimensions: detection, protection, response and management. The framework of the proposed metric framework is verified by experiments.
Academic Network Representation Learning Based on Metapath Tree
Authors: Zhang Wei , Liang Ying , Dong Xiangxiang 
Published: September 2019

AbstractNetwork representation learning aims to use low-dimensional dense vectors to represent nodes in the graph, which can reflect the graph structure and can be used in a variety of machine learning tasks. The academic network contains richer information, which most of the current methods are unable to capture. This paper proposes a method to get better vectors in the academic network. This method first uses the metapath tree to guide the random walk process, and adds a sampling process to preserve multiple metapath information. The vector representation of the nodes is obtained by training using the skip-gram model on the academic network. Experiment results show that the proposed model outperforms several traditional network representation learning models in multi-label classification and clustering tasks.
Dynamic Adaptive Piecewise Linear Representation Approach Based on Streaming Time Series
Authors: Luo Wei , Zhan Peng , Zhang Qi , Ji Cun , Zheng Jiecai , Li Xueqing 
Published: September 2019

Abstract
With the burgeoning of IoE (Internet of Everything), massive numbers of IoT devices in extensive fields are continuously producing huge number of streaming time series. The high dimensionality and dynamic uncertainty of this kind of data lead to the main challenge on traditional time series data mining research. Accordingly, time series representation methods have been regarded as a necessary pre-processing tool to provide data support for the follow-up time series data mining research. In this paper, we propose a novel dynamic time series representation approach called dynamic adaptive piecewise linear representation (DAPLR) for streaming time series, which can automatically provide a series of piecewise linear representation results to meet the diverse needs of different users.
An Improved Fingerprint Algorithm Based on Wireless Sensor Networks
Authors: Cheng Long , Liu Ze , Feng Liang 
Published: September 2019

Abstract
With the wide application of positioning technology in real life, people have particularly become concerned about the improvement of the accuracy of positioning. One of the common methods to deal with such problems is wireless sensor networks (WSN). Reducing the non-line-of-sight (NLOS)error and optimizing the positioning accuracy are the main technical problem. In this paper, we propose an improved fingerprint algorithm to enhance the accuracy of positioning. The traditional k-Nearest Neighbor (KNN) algorithm has the problem of sample imbalance, which leads to the individual data directly determining the decision result. Our proposed algorithm can effectively solve the problem of sample imbalance. Simulation results and experimental results illustrate that our algorithm is superior to KNN algorithm.
Development of ERP Financial Statement Evaluation System
Authors: Zhi Yuyang , Qian Cheng , Li Caixia , Li Jimei 
Published: September 2019

Abstract
With the sustained development of national economy, more and more enterprises begin to transform to informatization. As an important part of enterprise informatization, ERP (Enterprise Resource Planning) makes the demand for ERP talents growing day by day. However, there are many problems in the existing ERP examination system, such as large workload, difficult to guarantee the quality of question-making, low quality of automatic marking. In financial statements, a unit grid of financial statements has both formulas and data. At present, there is no system to evaluate the formulas, which creates great difficult for automatic setting questions and marking. These problems seriously affect the quality and efficiency of enterprise’s talent selection for ERP financial statements. This system is based on Yonyou ERP financial statement module, using MVC framework, C#, SQL Server and python technology to develop ERP financial statement skills evaluation system, which mainly solves the problem that it could not automatically generate test papers and mark the papers in financial statement evaluation system at present.
Classification of H&E Stained Breast Cancer Histopathology Images Based on Convolutional Neural Network
Authors: Jiang Yun , Chen Li , Zhang Hai , Xiao Xiao 
Published: September 2019

AbstractAutomatic classification of H&E breast cancer histopathology images is a challenging task. Computer-aided diagnostic systems help reduce costs and increase the efficiency of the process. Although the existing research on breast cancer histopathology image classification is higher than 90% accurate in the binary classifications (non-carcinoma/carcinoma), the classification accuracy of four classifications (normal, benign, in situ, invasive) is less than 80%. This paper proposes a framework for the classification of H&E stained breast cancer histopathological images, which includes two methods based on convolutional neural network. The first method is based on the convolutional neural network structure of the SE-ResNet module, and the second method is based on the transfer learning hybrid model structure, which achieves the accuracy of 80.33% and 86.11% respectively. Compared with the state-of-the-art method, the accuracy is improved by 2.56% and 8.33% respectively. The proposed framework achieves 91.67% accuracy in binary classification and is competitive with state-of-the-art methods.
An Improved Agent Strategy Training Method Based on DIAL
Authors: Liu Runzhi , Guo Baolong , Li Qianying , Wang Biyan 
Published: September 2019

Abstract
The traditional multi-agent method has a slow convergence speed and needs to train many parameter groups. If no algorithm optimization is performed, it takes a lot of time to reach a large number in order to achieve optimal results in the whole process. We optimize the parameter settings in the traditional algorithm training process, accelerate the convergence speed of the intelligent body in learning, and achieve the convergence effect faster.
Image Inpainting for Digital Dunhuang Murals Using Partial Convolutions and Sliding Window Method
Authors: Chen Ming , Zhao Xudong , Xu Duanqing 
Published: September 2019

Abstract
It is a difficult and challenging task to restore the digital murals to a visually pleasant result, and even the result is similar to the original murals without corruption. In this paper, to address the above problem, we propose an image inpainting strategy called PCSW for digital Dunhuang murals using partial convolutions and sliding window method. Specially, a deep neural network based on partial convolutions is used as the underlying model for image inpainting. Because the murals are somewhat damaged or even large areas are missing, in addition, digital murals are large and high resolution, it is unreasonable and impractical to use the original digital murals for training and then restoring the missing areas. Therefore, a data augmentation method based on sliding window technique is applied to increase samples and then improve the model accuracy. Experimental results have shown that the proposed strategy has a certain effect on the restoration of digital Dunhuang murals.
Deep Convolutional Neural Network for Air Quality Prediction
Authors: Mao Yushun , Lee Shiejue 
Published: September 2019

Abstract
In this paper, we tackle air quality forecasting by using deep learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter PM2.5 and sulfur dioxide). Deep learning (DL), as one of the most popular techniques, is able to efficiently train a scalable model on big data by optimization algorithms. The model is trained for air quality prediction with time series data. Our method takes the deep convolutional neural network (CNN) as the sequence module and inputs the time series data into the CNN model in turn for training. CNN is composed of many functional layers, such as convolution, pooling and ReLU. Convolution layer can effectively extract the sequential features of time series data. Sequential features work better than general features of time series data. Down-sampling in CNN is performed by the Pooling layer. Experimental results show that CNN performs well for air quality prediction.
Identity Recognition Based on Face Image
Authors: Qin Chen , Lu Xiaobo , Zhang Pengguo , Xie Huibin , Zeng Wenbin 
Published: September 2019

Abstract
This article is aimed at designing a human identity recognition algorithm based on face image, which will be used in indoor environment. As the working environment is set as indoor environment, the camera will not be affected much by illumination variation. The key is how to detect human face and handle the variation of facial pose. This article divides the whole recognition process into 4 parts: image pre-processing, face detection, face alignment, feature extraction and comparison. Face detection and feature extraction are the core functions and both realized by deep learning. The process of the whole algorithm can be described as following: images after pre-processing are fed to face detection network to get the locations of face and face landmarks. Then face alignment will be conducted. Finally, deep features of face will be extracted and compared. The unique features of this algorithm are its good performance of handling the variation of facial pose and its clear framework which allows the whole method can be easily adjusted and upgraded.
Binocular Vision Pose Estimation Based on PSOPF
Authors: Fei Yu , Fu Qiang , Zhuang Ying , Liu Xiang , Liao Gonglei , Zhao Qichun 
Published: September 2019

Abstract
In order to realize robot autonomous localization and navigation, binocular vision localization based on particle swarm optimized particle filter (PSOPF) is put forward according to the characteristics of nonlinear and non-Gaussian distribution complex system. The localization of 6-DOF robot only depends on binocular vision in the method. At first, road signs are obtained by SIFT feature matching points of binocular vision. The second, initial pose estimation is obtained by four elements. Finally, robot pose is estimated accurately by PSOPF, and the algorithm overco mes the shortcoming of particle filter (PF) and imp rove estimation accuracy. Experiment results show that this algorithm has high computing accuracy and robustness.
Research on Enterprise Risk Identification and Early Warning System based on Big Data Background
Authors: Zeng Weiyang , Jin Yining 
Published: September 2019

Abstract
Big data is a powerful tool which can modernize governance systems and improve governance for current public governance. Based on that, this paper discussed the research on situation of enterprise risk identification and early warning system under the background of big data. At first, the paper integrated the definitions of risk and enterprise risk management by domestic and foreign experts. Secondly, the author put forward that the development of risk identification and intelligent early warning is inevitable in the era of big data, and gave suggestions for improving the early warning system. Finally, the paper described the prospects of risk identification and early warning systems in the context of big data in the future. In summary, it is important to establish a closed-loop management chain of data security protection.
Empirical Study of the Impact of QFII Shareholdings on Board Governance and Corporate Performances ——Based on Chinese Listed Companies Data
Authors: Ren Xiaoyan , Yang Shuili 
Published: September 2019

Abstract
Board governance structure is introduced into this paper as mediating variables to analyze the indirect impact of QFII shareholdings on corporate operating performances. The data of QFII shareholdings ratio between 5% and 30% of Shanghai and Shenzhen A-share listed companies from 2008 to 2016 after the completion of China’s split-share structure reform are selected to carry out empirical study. Empirical results indicate that the mediating effects of proportion of foreign directors, separation degree of ownership and management right of board, president and general manager being one person or not, proportion of independent directors and senior managers’ shareholdings ratio of board governance structure are significant. However, the mediating effect of the numbers of “four committees” of board is not significant.
EMD and KPCA-based Speckle Suppression in SAR Images
Authors: Wu Chun , Ma Xiaoyan , Wang Wenbo 
Published: September 2019

Abstract
This paper proposes a speckle suppression method for synthetic aperture radar (SAR) images based on empirical mode decomposition (EMD) and kernel principal component analysis (KPCA) following three steps: first, SAR image after logarithmic transformation is decomposed by EMD; second, noise in each intrinsic mode function (IMF) is further removed by KPCA; finally, the denoised SAR image is obtained by accumulating the IMFs processed by KPCA. In the second step, IMF, decomposed by KPCA, is reconstructed by the selection of appropriate principle components according to noise energy proportion, which is approximately calculated based on the statistical properties of speckle noise and energy distribution model of EMD-decomposed Gaussian white noise (GWN). Experimental results show that, compared with traditional EMD-based image denoising algorithms, the proposed method is superior in both speckle suppression and detail information retention.
Research on the Construction of Smart Town Based on Artificial Intelligence Technology
Authors: Wei Wei , Lin Qian 
Published: September 2019

Abstract
With the gradual rise of the characteristic towns, most cities in the country are planning and creating local characteristic towns. In recent years, themes such as “Internet Plus”, “Smart Plus”, and artificial intelligence have been well known. People’s lives are full of intelligence, networking, and high-end, and intelligence has also been incorporated into the construction of characteristic towns. Based on the analysis of the background of the construction of smart towns, this paper analyses and summarizes the nine aspects of its construction, and expounds the application of artificial intelligence technology in the construction of smart towns from the aspects of industry, life and tourism, with the hope that the builders are small. In the process of town creation, the goal of building a smart town can be established, and artificial intelligence technology can be applied as much as possible.
Research on the Shunting Precision Forging of Ribbed Plate Gear Blank Based on DEFORM-3D Finite Element Analysis Under the Framework of New Engineering
Authors: Zhang Hui , Pan Aiqiong , Zhang Yanqin , Zhang Zhanling , Liu Jun 
Published: September 2019

Abstract
In order to solve these problems in the precision forging, such as non-saturate, folding, high energy consumption and low material utilization, a certain type motor vehicle of ribbed plate gear blank was selected as the main focus. Based on the principle of shunting and the law of minimum damping, the finite element model of shunting precision forging was established by using finite element numerical simulation software DEFORM-3D, and the temperature field, equivalent strain field and workpiece were analyzed in depth. The results show that the end faces of ribbed plate are flat and there are no folding defects at the bottom. The influence of damage and forming load on forming process and quality provide theoretical basis and technical support for shortening R&D cycle and optimizing reasonable process parameters in actual production.
Parameter Optimization of Hot Stamping for an Extension Part Based on DYNAFORM under the Background of New Engineering
Authors: Pan Aiqiong , Zhang Hui , Liu Jun , Zhou Wei 
Published: September 2019

Abstract
Based on the finite element analysis software eta/DYNAFORM, the hotstamping mo del of the front longitudinal beam on an extension part was established. The finite element numerical simulation of the beam was carried out by the method of thermal coupling. The effects of different stamping speed, holding time, and temperature field and friction factor on forming quality were analyzed. The results show when the stamping speed of 200 mm/s, the holding time of 6s and the friction factor of 0.4, the martensite content is relatively higher and the springback is smaller. It provides an important basis for reasonable technological parameters of ultra-high strength parts, and also provides technical support for the new engineering construction.
Analysis of Low-Altitude Airspace
Authors: Qiu Shidai , Yao Dengkai , Wang Zekun 
Published: September 2019

Abstract
Low-altitude airspace resources have been the bottleneck of the development of general aviation industry and one of the key factors restricting the development of general aviation in China. Through theoretical research and on-the-spot investigation, this paper constructs a low-altitude airspace resource availability evaluation index system, uses multiple linear regression method to carry out systematic analysis, and determines the index weights at all levels. Taking Jiangsu Province as an example, the present situation of low-altitude available resources in Jiangsu Province is deeply analyzed, the actual situation and a large amount of data in various regions are summarized, the influence values of various factors on the region are calculated, and finally the availability results of low-altitude airspace resources are obtained. The research results demonstrate the scientificity and effectiveness of the method, which can provide reference for the rational utilization of low-altitude airspace resources and the declaration of general aviation activity airspace in China.
The Research and Engineering Practice in Air Clearance of Valve Hall and DC Field in VSC-HVDC Converter Station with High Voltage and Large Capacity
Authors: Zhang Libin , Chen Xiangyu , Fu Shouqiang , Li Hongjian , Zhao Min 
Published: September 2019

Abstract
The value of air clearance is an important basis for the design of the converter station, which affects the layout and safe operation of the converter station. In this paper, the calculation method of minimum air clearance based on improved clearance shape coefficient is proposed, and the g-parameter method is used to correct the discharge characteristics of air gap under non-standard meteorological conditions. The method is applied to the valve hall and DC field air clearance design of Zhangbei ±500 kV flexible DC converter station. The Zhangbei converter station is used as a typical representative to empirically analyze the influence of temperature and humidity changes on the air gap, and we can get the temperature and humidity value when air clearance are at the maximum value, and we calculate the air clearance of different gap shape coefficients. This paper can provide method guidance and data for the selection of air clearance for other similar projects.
A DMA Interruption Mechanism for Multi-Robot Communication and Location
Authors: Zeng Xiangjin , Huang Le , Jin Zilong , Zhang Like 
Published: September 2019

Abstract
For multi-robot communication and positioning in lunar environment, we put forward an improved MOBUS-RTU communication networking protocol. The protocol adopts the cooperation mechanism, whichuses the sending and receiving of Direct Memory Access (DMA) and idle interruption of DMA. The data length is not limited for communication. Besides, the message is automatically stored in the cache and processed after receiving. This method greatly improves the utilization efficiency of the CPU. Finally, the corresponding communication and positioning test platform is developed based on STM32f103, which the system communication, positioning accuracy, packet loss rate and other experimental tests are carried out. The results show that the design meets the application requirements.
Application Research of Virtual Reality Technology in Landscape Design
Authors: Liu Yue , Wang Bo 
Published: September 2019

Abstract
With the development of virtual reality technology, its application in various fields of society has become more and more extensive. The application of virtual reality technology in landscape design is a very typical example. Virtual reality technology uses information science and technology to simulate a virtual three-dimensional space, giving people a new sensory experience in terms of sight, hearing and touch. The application of virtual reality technology in landscape design allows people to observe the landscape of three-dimensional space in an all-round and unrestricted manner, which greatly enhances the quality of landscape design, and also enables people to further understand and understand virtual reality technology. However, it should be noted that in the process of applying virtual reality technology for landscape design, virtual reality technology and information technology should be combined to achieve the purpose of three-dimensional image, three-dimensional and system integration technology integration, and then complete the landscape design task. This paper mainly analyzes and studies the application process and characteristics of virtual reality technology in landscape design and the corresponding precautions, in order to better apply virtual reality technology and landscape design.