bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Electromagnetic Immunity Design of Coal Mine Instruments Based on Wireless Sensors
Authors: Li Bojun 
Published: November 2019

Abstract
For a long time, one of the factors that perplex the safety problem of coal mine is the interference of electromagnetic signals on underground instruments, which affects the normal work of underground instruments. In this paper, we firstly elaborate the causes and hazards of electromagnetic interference on coal mine instruments, and then explore the electromagnetic immunity characteristics of wireless sensors. Finally, we propose to construct an electromagnetic shielding model for coal mine instruments with wireless sensor, and discuss on design of hole and joint structure and slot array spacing of the shielding cavity. We conclude that a shielded body with a symmetrical form of open-cell structure, such as circular and positive, generally has higher shielding effectiveness, and to increase the gap spacing of the shielding cavity can slightly improve the shielding effectiveness.
Histograms of Oriented Gradients for cats-dogs detection
Authors: Wu Jiaxing , Yang Zixuan , Wang Ting 
Published: November 2019

Abstract
We study the question based on grids of Histograms of Oriented Gradient (HOG) and support vector machine(SVM),adopting different kernel functions to distinguish cat from dog with robust visual object recognition [1]. We show experimentally that the quadratic descriptors significantly outperform others kernel functions and with the increasing number of the samples, the performance of the quadratic is getting better and better. Beside, in order to reduce the complexity, we also apply principal Component Analysis, which is known as pca, and we also study the influence of the number of features on performance, concluding that the line is quadratic.
Research on Hodoop Job Scheduling Algorithms Based on Dynamic Fusion
Authors: Wang Lihong 
Published: November 2019

Abstract
Adaptive genetic algorithm and improved ant colony algorithm are combined to solve Hadoop job scheduling problem. Firstly, the global search ability of the adaptive genetic algorithm is used to generate the list of resources allocated by the task. When the search speed of the genetic algorithm gradually decreases, the optimal integration time of the adaptive genetic algorithm and the ant colony algorithm is determined dynamically. The initial pheromone distribution of the ant colony algorithm is generated from the optimal solution solved by the adaptive genetic algorithm. Improve the target node selection strategy of ant colony algorithm, consider the success rate of completing tasks of nodes, and accelerate the speed of ant colony algorithm to solve the optimal solution. Simulation results show that compared with genetic algorithm and ant colony algorithm, hybrid genetic algorithm takes less time, and the more tasks, the more obvious advantages.
Square-Root Divided Difference Attitude Filter for SOTM Using Low Cost Sensors
Authors: Shen Xiaowei , Jin Wei , Chang Ruihua , Jia Weimin 
Published: November 2019

Abstract
In order to isolate the antenna pointing from disturbance of vehicle, an attitude stabilization method based on the MEMS inertial sensors and single baseline GPS is studied. Taking quaternion and drift of gyroscopes as state vector, a square-root DDF is used to combine the information from the inertial sensors with the heading attitude information derived from GPS. Aiming to different update frequency between accelerometers and GPS, two measurement equations with two different update frequencies are chosen. Then, with the estimated attitude information, a strap-down stabilization method for angular position is used to isolate disturbance of vehicle. The results show that DDF achieves good estimation accuracy, which can meet the demand for the attitude stabilization of mobile communication.
The Service of Secure Data Publication and Subscription on Cloud Platforms
Authors: Chen Guangyu , Liu Jian , Xian Ming 
Published: November 2019

Abstract
With the increase of data volume, the service of data publication and subscription is an efficient method to share massive data. It is impossible to capture and manage data with traditional database tools. Therefore, the cloud platform becomes the most suitable platform for data publication and subscription due to its huge storage, strong computing power and good economical utility. In the process of data publishing and subscription, cloud server as a third party is semi-honest, which will bring serious privacy problems. In this paper, a secure data publishing and subscription scheme based on cloud platform is proposed, which can protect the privacy of data and subscriber interest, and achieve efficient and accurate data publishing and subscription. It can realize that only data subscribers who meet the requirements of access rights can obtain and decrypt the data, and the matching of data subscriber interest and data label can be realized at the same time. In addition, this scheme also supports user cancellation, it can cancel dishonest users in the system.
Research on image style transfer method based on Generative Adversarial Networks
Authors: Wu Jie , Duan Jin , Yu Jinqiang , Shi Haodong , Li Yingchao 
Published: November 2019

Abstract
Image style transfer technology refers to converting an image into a similar image to the famous painting style by learning the famous painting style. The technology is mainly divided into two categories: one uses neural networks to achieve single-image style transfer, and the other uses multi-image style transfer implemented against the generation network, which can learn about mismatched image sets and realize the transfer of a writer’s overall work style. However, there is still room for improvement in training time and picture quality. The improvement mainly focuses on the introduction of the edge Gaussian smoothing process to preprocess the style image set, so that the generated image can preserve the clear edges of the original image. Improving the traditional residual element structure in the generation network reduces the calculation of network parameters to a certain extent, improves the calculation speed, and accelerates the convergence of the model.
Application research on Interner access of smart home network based on 6LoWPAN
Authors: Tianzhu Gao , Xiwei Feng , Yizi Dou , Lei Ma , Rui Zhu , Yanbing Wu 
Published: November 2019

Abstract
With the rapid development of Internet of things technology and the increasing diversity and number of devices connected to home Network, the technology of 6LoWPAN (IPv6 over low-power Wireless Personal Area Network) has been widely used in smart home Network. According to the actual needs of users, it will be of more practical significance to connect the smart home network to the Internet (IPv4) so that users can obtain the information in the home network instantly and realize remote control through the mobile application platform.Based on 6 lowpan overall design scheme of smart home network access to the Internet, including the system gateway hardware and software design, and combining the transition technology analysis of the 6 lowpan network access of the Internet technology difficulty, according to the current technical background, we have analyzed based on 6 lowpan technology of smart home network to access the Internet in the next generation Internet development foreground and research significance.
Improved Session-Based Recommendations Using Recurrent Neural Networks for Music Discovery
Authors: Ye Yi , Xie Yi , Chen Cheng 
Published: November 2019

Abstract
The number of songs on the music platform is large while users just have access to a fraction of songs. Recommendation systems are created to solve this problem and collaborative filtering is the most widely used method. But collaborative filtering methods are overly dependent on user ratings of projects. New recommendation methods, Deep Learning techniques, are proposed especially for items’ feature extraction and for session-based recommendation with RNN. In this paper, we considered a LSTM-based approach for session-based recommendation and built architecture for music discovery. This architecture is composed of four modules: MDM (Music Data Modeling), NSP (Next Song Prediction Using LSTM), MLB (Music Library Building) and R4U (Recommendation for Users). Improved session-parallel mini-batches and ranking loss function are leveraged by the architecture to modify the basic LSTM. Through the experiment, we obtained a relative improvement in ranking metrics (MRR@N and Recall@N) over the session-based algorithms baselines.
Natural Scene Chinese Character Text Detection Method Based on Improved CTPN
Authors: Zeng Wenxian , Meng Qinglin , Zhang Shuqing 
Published: November 2019

Abstract
Text detection in natural scenes, due to differences in size, font, line direction, lighting conditions, text weakness and image background complexity, plays an important role in the research field and remains a challenging and important topic. We have improved the CTPN text detection network and changed the Side-refinement detection box to determine the scaling mechanism. And based on the experiment, change the LSTM network to the GRU neural network. In the dataset of Chinese character text game in natural scene released by Meituan, it reached 0.78 F1-Measure, which reached 0.89 and 0.61 F1-Measure on the ICDAR 2013 and 2015 data sets respectively. Compared to the 0.88 and 0.61 F1-Measure in the CTPN article, there is a big improvement.
Application Status of Big Data in Agriculture and International Cooperation
Authors: Jie HU , Jiabin WANG , Zhongjun DU , Xiaoguang LIU 
Published: November 2019

Abstract
This paper summarizes the current research background and application status of big data, systematically describes the domestic development of the concept and technology of big data, introduces the international advanced big data technology and the mainstream technology and methods of big data in China’s agricultural field, and the technology and application status of big data in the field of international cooperation; and discusses the existing problems. Finally, it is systematically proposed that the big data of international cooperation in agriculture should be applied to the research of international cooperation in agriculture, the application of big data in international cooperation in agriculture, and the application of big data in international cooperation in agriculture should form a solid triangular structure.
Expanding Receptive Field YOLO for Small Object Detection
Authors: Du Zexing , Yin Jinyong , Yang Jian 
Published: November 2019

Abstract
State-of-art object detection networks like YOLO, SSD and Faster R-CNN all have achieved great success in object detection. However, these algorithms have a low performance in small object detection. So, we produce the Expanding receptive field YOLO (ERF-YOLO) to deal with this problem. At first, we propose an efficient block which is called expanding receptive field block (ERF-block) to capture more information in larger areas. Base on YOLOv2, we down-sample the low-level location information by ERF-block, and up-sample feature information by deconvolution. Then we further assemble these two parts together to make the prediction. After training the network on VOC dataset, we have a good result with 82.6% mAP (mean Average Precision) which is 4.0% higher than the original YOLOv2 network. Thanks to the efficient block, it takes 62fps to detect one image when the input size is 416×416, which could keep a real-time speed. In addition, we also evaluate the model on a remote sensing dataset which contains many small targets, and it also shows that ours model has a better performance.
Research of Several Multicarrier Transmission Technologies in Mobile Communication
Authors: Wendi Xie , Lin Yao , Jing Wang 
Published: November 2019

Abstract
This paper describes the basic principles of OFDM(Orthogonal Frequency Division Multiplexing), FBMC(Filter Bank Multi-Carrier), UFMC(Universal Filtered Multi-Carrier), GFDM(Generalized frequency division multiplexing) and F-OFDM(Filtered-Orthogonal Frequency Division Multiplexing) in detail, and compares the performance characteristics and computational complexity of the five new modulation technologies. Studies have shown that, except for OFDM, the other four types of out-of-band leakage all are low, because FBMC and UFMC do not use CP, so they have high time-frequency efficiency, but the complexity is also increased accordingly. GFDM is a kind of independent block modulation with flexible frame structure, good robustness, lower complexity and good applicability. F-OFDM has fast sidelobe attenuation and high spectral efficiency, which can greatly suppress adjacent band interference through sub-band filter, meeting the needs of 5G rich service scenarios.
RETRACTED: Development and Application of Computer Software Based on the Background of Big Data Era
Authors: Liu Wei , Ma Mingrui , Ren Ripeng 
Published: November 2019

Abstract
With the rapid development of computer technology in the era of big data, this paper discusses the development and application of computer software. Firstly, it explains the definition of the era of big data, then analyses the development of computer software technology in China; finally, it discusses the specific application of computer software, and points out the main problems in the application process. The purpose is to provide some references for the development and utilization of computer software to ensure that computer technology can develop steadily in big data era.
Application and Research of Computer Big Data Based on Structure in Internet Learning
Authors: Zhaohongbo  , Yanweiyu  , Caozhiyong  
Published: November 2019

Abstract
In recent years, the rapid development of the Internet and information industry has promoted the related technological development and innovation, and brought mankind into a new era of big data. Big data provides a great convenience service for people. It can not only realize the sharing of resources, but also enable people to obtain a large amount of information. The study and application of the Internet is becoming more and more important today. Computer big data, a technology closely related to the development of the Internet, plays an important role in Internet learning. The continuous popularization of the Internet and the continuous improvement of its speed have narrowed the distance between people and made various information knowledge more concentrated and rich. Big data can promote significant improvements in Internet learning efficiency with its own capabilities. Applying big data technology in Internet learning can help learners to acquire knowledge more quickly and conveniently, and can also continuously enrich the application of big data.
Mobile localization in complex indoor environment based on ZigBee wireless network
Authors: Wang Zhu , Liu Miaomiao , Zhang Yongsheng 
Published: November 2019

Abstract
An algorithm for mobile robot localization in complex indoor environment is introduced in this paper. The robot is designed as a special node of ZigBee network, so the robot localization is transformed into the location of ZigBee mobile node. For reducing error, the localization method adopts and improves the RSSI ranging technology. Further, the distance is corrected by the least square method, and the three sides’ measurement is used to calculate the coordinates of the robot. The experiments were carried out in two environments: narrow and complex laboratory, and spacious hall. The results show that the system can realize the function of data acquisition, transmission, processing and display. It can satisfy the localization requirement in indoor environment. Compared with the traditional algorithm, the maximum positioning error is reduced.
Research on Classroom interaction mode based on Artificial intelligence technology
Authors: Tang Mingwei , Tan Song , Pan Xiaoge , Wang Xiaodi , He Mingxing , Chen Xiaoliang 
Published: November 2019

Abstract
Artificial intelligence (AI) technology has an effect on various industries at an unprecedented speed. The development of artificial intelligence has brought hope for the university educational reform in the world. But, will AI bring the subversive innovation of education for students? In order to answer this question, the paper first analyses the demand for innovation in education and clarifies the conditions for subversive innovation. Then, the application of artificial intelligence in the field of education is introduced in detail, on the basis of which the interactive model system interconnection of educational artificial intelligence supported by artificial intelligence is presented visually. Finally, the data of the experiment prove the effectiveness of the model from 2016 to 2019.
A Summary of the Application of Block Chain in Accounting and Finance System
Authors: Zhu Yangpeng , Zhao Shumin 
Published: November 2019

Abstract
Block chain is the most popular technology in the field of Internet finance, which has brought profound changes to the financial industry. Based on the principle of block chain technology and the business activities of enterprises, this paper combines block chain technology with accounting and financial system, solves the problems of traditional accounting processing, such as many links, long time-consuming and high financial risk, simplifies the cumbersome procedures of accounting processing, and improves the accuracy of accounting information, promotes the standardization and standardization of accounting and accounting letters and comparability and fairness of interest rates.
Analysis of Application of Big Data in College Education Management
Authors: Wang Lei 
Published: November 2019

Abstract
As big data technologies gradually mature, big data and the field of higher education are more and more closely merged. By analyzing the characteristics of data and application process of higher education, it is found that the big data plays a supporting role in education policy-making, operations management of teaching processes, education evaluation and learning autonomy of students. However, universities should focus on the problems of security management, privacy protection and business model that exist in the application of big data technologies. Additionally, universities could establish security defense architecture of big data, integrative data platform and U-G-E (university, government, enterprise) multiparty collaborative innovation to protect data and solve those problems.
Controlled autologous recellularization and inhibited degeneration of decellularized vascular implants by side-specific coating with stromal cell-derived factor 1α and fibronectin
Authors: Sugimura Yukiharu , Chekhoeva Agunda , Oyama Kyohei , Nakanishi Sentaro , Toshmatova Mahfuza , Miyahara Shunsuke , Barth Mareike , Assmann Anna Kathrin , Lichtenberg Artur , Assmann Alexander , Akhyari Payam 
Published: November 2019

Abstract
Optimized biocompatibility is crucial for the durability of cardiovascular implants. Previously, a combined coating with fibronectin (FN) and stromal cell-derived factor 1α (SDF1α) has been shown to accelerate the in vivo cellularization of synthetic vascular grafts and to reduce the calcification of biological pulmonary root grafts. In this study, we evaluate the effect of side-specific luminal SDF1α coating and adventitial FN coating on the in vivo cellularization and degeneration of decellularized rat aortic implants. Aortic arch vascular donor grafts were detergent-decellularized. The luminal graft surface was coated with SDF1α, while the adventitial surface was coated with FN. SDF1α-coated and uncoated grafts were infrarenally implanted (n = 20) in rats and followed up for up to eight weeks. Cellular intima population was accelerated by luminal SDF1α coating at two weeks (92.4 ± 2.95% versus 61.1 ± 6.51% in controls, p < 0.001). SDF1α coating inhibited neo-intimal hyperplasia, resulting in a significantly decreased intima-to-media ratio after eight weeks (0.62 ± 0.15 versus 1.35 ± 0.26 in controls, p < 0.05). Furthermore, at eight weeks, media calcification was significantly decreased in the SDF1α group as compared to the control group (area of calcification in proximal arch region 1092 ± 517 μm2 versus 11 814 ± 1883 μm2, p < 0.01). Luminal coating with SDF1α promotes early autologous intima recellularization in vivo and attenuates neo-intima hyperplasia as well as calcification of decellularized vascular grafts.
Electrospun polyurethane-based vascular grafts: physicochemical properties and functioning in vivo
Authors: Gostev Alexandr A , Chernonosova Vera S , Murashov Ivan S , Sergeevichev David S , Korobeinikov Alexandr A , Karaskov Alexandr M , Karpenko Andrey A , Laktionov Pavel P 
Published: November 2019

Abstract
General physicochemical properties of the vascular grafts (VGs) produced from the solutions of Tecoflex (Tec) with gelatin (GL) and bivalirudin (BV) by electrospinning are studied. The electrospun VGs of Tec-GL-BV and expanded polytetrafluoroethylene (e-PTFE) implanted in the abdominal aorta of 36 Wistar rats have been observed over different time intervals up to 24 weeks. A comparison shows that 94.5% of the Tec-GL-BV VGs and only 66.6% of e-PTFE VGs (р = 0.0438) are free of occlusions after a 6 month implantation. At the intermediate observation points, Tec-GL-BV VGs demonstrate severe neovascularization of the VG neoadventitial layer as compared with e-PTFE grafts. A histological examination demonstrates a small thickness of the neointima layer and a low level of calcification in Tec-GL-BV VGs as compared with the control grafts. Thus, polyurethane-based protein-enriched VGs have certain advantages over e-PTFE VGs, suggesting their utility in clinical studies.
Learning and flexibility for water supply infrastructure planning under groundwater resource uncertainty
Authors: Fletcher Sarah , Strzepek Kenneth , Alsaati Adnan , de Weck Olivier 
Published: November 2019

Abstract
Water supply infrastructure planning in groundwater-dependent regions is often challenged by uncertainty in future groundwater resource availability. Many major aquifer systems face long-term water table decline due to unsustainable withdrawals. However, many regions, especially those in the developing world, have a scarcity of groundwater data. This creates large uncertainties in groundwater resource predictions and decisions about whether to develop alternative supply sources. Developing infrastructure too soon can lead to unnecessary and expensive irreversible investments, but waiting too long can threaten water supply reliability. This study develops an adaptive infrastructure planning framework that applies Bayesian learning on groundwater observations to assess opportunities to learn about groundwater availability in the future and adapt infrastructure plans. This approach allows planners in data scarce regions to assess under what conditions a flexible infrastructure planning approach, in which initial plans are made but infrastructure development is deferred, can mitigate the risk of overbuilding infrastructure while maintaining water supply reliability in the face of uncertainty. This framework connects engineering options analysis from infrastructure planning to groundwater resources modeling. We demonstrate a proof-of-concept on a desalination planning case for the city of Riyadh, Saudi Arabia, where poor characterization of a fossil aquifer creates uncertainty in how long current groundwater resources can reliably supply demand. We find that a flexible planning approach reduces the risk of over-building infrastructure compared to a traditional static planning approach by 40% with minimal reliability risk (<1%). This striking result may be explained by the slow-evolving nature of groundwater decline, which provides time for planners to react, in contrast to more sudden risks such as flooding where tradeoffs between cost and reliability risk are heightened. This Bayesian approach shows promise for many civil infrastructure domains by providing a method to quantify learning in environmental modeling and assess the effectiveness of adaptive planning.