bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Regional Medical Heterogeneous Data Integration Technology
Authors: Zhang Chunfei , Zhang Yuchun , Duan Yunna 
Published: November 2019

Abstract
The integration of regional medical heterogeneous information is a problem that must be solved in the process of modern medical digitization. Based on the analysis of the characteristics of the existing medical information system, combined with the existing data integration and exchange technology, this paper proposes a heterogeneous data integration scheme for regional medical care. It also focuses on the data processing in the process of regional medical data integration and exchange, and describes in detail the process of medical data extraction, medical data conversion and medical data loading. The goal is to integrate existing heterogeneous medical information systems and eliminate information barriers to improve the effective utilization of medical information.
An Encoder-Decoder Neural Network for Indefinite Length Digit Sequences in Natural Scene Recognition
Authors: Zhang Tong , Zhang Erhan , Zhang Hanfeng , Shen Feihong , Guo Dongwei , Lin Kaichao 
Published: November 2019

Abstract
Extracting text information from raw images has always been a hot and difficult problem in computer vision research. Due to blurred image, uneven illumination and complex backgrounds, etc., the recognition of natural scene digit is difficult to achieve desired results. In this paper, an encoder-decoder neural network model is proposed to solve the problem of recognition of indefinite length digit sequences in natural scene. The encoder is convolutional neural network (CNN), and the decoder is long short-term memory (LSTM). The encoder accepts a fixed-format image and outputs a fixed-length feature vector; the decoder accepts the feature vector and outputs a predictive sequence of indefinite length. The verification based on Google Street View house number Dataset (SVHN) shows that our method has a good performance. After 10 hours of training, whose accuracy is 96.57%.
Application of Wavelet Transform in Image Super-Resolution
Authors: Zhao Yucong , Zhu Xiaonan , Wang Ping , Wang Yuanchen , Ma Jiquan 
Published: November 2019

Abstract
Most of learning-based super-resolution (SR) methods reconstructed high resolution (HR) image using a very deep convolutional neural network (CNN). CNN-based methods are used to process low resolution images, the predicted image is too smooth and some texture details are ignored. In order to improve the quality of the recovered high resolution image, wavelet transform is applied at the end of our model. Firstly, the prediction image is obtained through the CNN model; then, the predicted image and the ground truth image are processed into four frequency channels by wavelet transform; at last, four frequency channels and original image are considered when calculating loss. Compared with the original CNN model, the results of adding wavelet transform is obviously improved.
Research on Identity Mechanism of Large-scale Drug Virtual Screening in Heterogeneous Multicore Environment and SMP Environment
Authors: Li Xiaohu , Zhang Zhiyu , Wang Xinru , Liu Hao , Wei Zhiqiang 
Published: November 2019

Abstract
This paper studies the mechanism of ultra-large-scale drug virtual screening in the heterogeneous multicore environment and SMP (Symmetric Multi-Processor) environment. Given the differences caused by different CPUs, we solved the incompatibility of the open-source molecular docking software Autodock Vina on two supercomputer systems in different environments and realized the automatic and intelligent large-scale virtual screening process for small-molecule ligands. At the same time, we propose a processing method of large-scale virtual screening using Autodock Vina in two environments, which makes full use of the dominant computing power of supercomputers to improve the efficiency of large-scale molecular docking and reduce the load of different supercomputer systems facing large-scale molecular docking tasks. Finally, we conduct a virtual screening experiment on the X86 platform supercomputer and the Sunway TaihuLight Supercomputer based on the heterogeneous multicore architecture CPU—SW26010. The experimental results show that our large-scale drug virtual screening method for different CPU environments is effective.
Management Evaluation of Distribution Network Based on Fuzzy Evaluation Matrix
Authors: Ran Chen , Kunhua Ji , Yun Wang , Jian Lu , Haining Xie , Liming Zhuang 
Published: November 2019

Abstract
Focused on automation management level of distribution network, an evaluation method based on fuzzy matrix is proposed in this paper. First, data extraction efficiency, data quality and automation ratio are selected as evaluation indicators. Then, by establishing an analytical model, each indicator is compared in pairs to construct a judgment matrix. And the consistency test of the matrix is done before the follow step. Next, through the experts’ scores of the influencing factors, fuzzy mathematics is used to make a comprehensive evaluation of the automation management evaluation indicators. Finally, according to the evaluation results, the automation management level of the distribution network is analysed. According to the above method, a certain urban area is taken as an example to obtain the quantitative index of the automation management level of the distribution network. It is of great significance to strengthen the distribution network construction with the cooperation management mode, so as to realize the comprehensive improvement of the distribution network management level.
Reliability Evaluation of Mechatronics Products Based on Intuitionistic Fuzzy Set Theory
Authors: He Zhiyuan , Lv Weimin , He Hailong 
Published: November 2019

Abstract
In view of the lack of test data for some unmonitored electromechanical products, this paper mainly proposes a multi-source information reliability assessment method based on Intuitionistic Fuzzy sets and evidence theory. The method first uses the group decision-making idea to obtain multiple evaluation information and distributes the evaluation information as the reliability of the evidence. Using the relationship between evidence theory and Intuitionistic Fuzzy sets, the reliability distribution is transformed into intuitionistic fuzzy numbers. Then a method for determining the degree of evidence support is proposed, and the reliability of the evidence information is evaluated by this method. Finally, the combination rules are improved and information fusion is performed. The case analysis proves the feasibility of the method.
Shape Feature Extraction Methods of Rodents in the Field Based on Machine Vision
Authors: Zhu Deli , Chen Bingqi , Han Liliang , Wang Yong , Wei Chaojie , Feng Jie 
Published: November 2019

AbstractIn order to obtain the shape features of rodents from the field, a feature extraction scheme based on machine vision was proposed. We design an experimental device with a camera. The camera placed on the top of the device to get the rodent image. After thresholding rodent image based on the global value, we get the binary image, and then the morphological erosion and dilation operation are used to denoise, and the contour are detected by the 8 adjacent pixels of a point. Chain code analysis method is used to track the contour. Multi-contour tracking extraction method is used to remove the non-rodent region. Based on the shape of the rodent tail that is slenderer than the body, we split the rodent’s tail from the body on the precise contour image. The length of the tail, the length of the body, the height of the body, the proportions of the tail and the body are extract based on contour analysis. Experiments show that this method can extract shape features of rodents accurately. It can provide basic data for analyzing the life habits and population evolution of field rodents.
Research and Analysis on Key Techniques of Digital Speckle Correlation Method
Authors: Dong Yi , Hong Liang , Wang GuiRong , Dong Yi 
Published: November 2019

Abstract
The registration of speckle images plays a key role in many fields, such as: material mechanics; accident monitoring; geodetic survey; missile guidance. Speckle image is composed of a large number of random spots, that it is difficult to obtain obvious feature values through feature extraction. Digital speckle correlation method is to directly extract the surface displacement and deformation information of objects from digital images, so all kinds of digital images can be used as processing objects. However, when the target image quality is poor or has a large deformation, its registration accuracy is difficult to reach the sub-pixel level. In this paper, we will study and analyze the key technologies of existing digital speckle correlation methods.
Research on High Availability Architecture of Cloud Platform
Authors: Xinming Lai , Haitao Wang , Jing Zhao , Fan Zhang , Chao Zhao , Gang Wu 
Published: November 2019

Abstract
Cloud computing platforms have extremely requirements for high availability, and many solutions have proposed to ensure high availability of system. In view of the shortcomings of the existing solutions, we improve the high availability of the cloud computing platform based on the 3 tiers cluster environment, including the business layer cluster, the management cluster, and the main control layer. The management cluster ensures high availability of the service layer cluster. The main control layer ensures high availability of the management cluster. The main control layer uses the dock mechanism to ensure certain high availability. Through practical application, the solution achieves high availability of the cloud computing platform, achieving availability 99.99%.
The Application of CAD/CAE Technology in the Teaching of vehicle Engineering Specialty
Authors: Chunxia Zhu , Tiying Wang , Mingwei Xiao 
Published: November 2019

Abstract
Good teaching method is an important way to improve teaching quality and cultivate students’ creative thinking. This paper mainly describes the application of CAD/CAE technology in vehicle engineering teaching, enriched teaching methods, can improve students’ ability of learning and engineering practice, improve teaching quality, and has good practical significance.
Fusion of Handcrafted and Deep Features for Medical Image Classification
Authors: Liu Dong , Liu Yaohui , Li Shanglin , Li Weiqing , Wang Luda 
Published: November 2019

Abstract
Medical image classification has recently attracted increased attention. Effective feature extraction and learning are key means to improve classification performance. However, in the current study, handcrafted feature are mostly designed with intuitive mode, while the deep feature depends on a large amount of training samples and has weak interpretability. To capture more discriminative features for medical image, a novel feature fusion approach, termed multi-layer visual feature fusion (MLVSF), has been proposed on the basis of low-level, mid-level and deep features. More specifically, by fusing the handcrafted and deep features generated by local binary pattern variant, bag-of-visual-words, convolutional neural network, respectively, MLVSF can effectively enhance the discriminating power of features for medical image recognition. Experimental results on two medical image datasets show that MLVSF can improve convolutional neural networks, and achieve a better classification accuracy in comparison with some state-of-the-art methods.
An Improved Active Contour Model in Medical Image Segmentation
Authors: Zhang Xuefei 
Published: November 2019

Abstract
Aiming at the problem that medical image segmentation has low precision because of restriction of image principle, this paper proposes an improved active contour model based on vector field convolution, and combining both the balloon force and gradient directional information. This method improves the energy function of model, has more chances to approximate the complex boundary. The experimental results show that the new model not only inherits the advantages of the previous models, but also show fast speed, limits the influence of false edge and noise disturbance.
Anomaly Detection for Water Supply Data using Machine Learning Technique
Authors: Fang Shu , Sun Weize , Huang Lei 
Published: November 2019

Abstract
The advent of the era of big data brings new challenges and opportunities to water data processing. Abnormal detection or authenticity verification of water supply data becomes an urgent problem in natural resource data processing. This article addresses the issue of anomaly detection for a set of measured water supply data that provided by the Ministry of Water Resources of China. The machine learning technique, namely, One-Class support vector machine, is used for anomaly detection, and a detailed analysis of the features of anomalies is also provided. Experiments on three situations, namely, one dimensional situation, three dimensional situation and seven dimensional situation are carried out to analyse the features of anomalies. The experiment results revealed that the cases with entries of large values more than 10 times of median, zero entries together with some large values and ascending or descending sequences tend to have high abnormality scores.
Overview of Research on Health Assessment and Fault Prediction of Complex Equipment Driven by Big Data
Authors: Jin Can-can , Zhang Hong , Liu Jun , Lu Yang 
Published: November 2019

Abstract
Health evaluation is an important content of health management for complex equipment, and fault prediction is a key link to realize condition-based maintenance. Based on the literature review, this paper gives a feasible modeling method: The health status of complex equipment is expressed as time series by constructing forward and backward LSTM model, and the health evaluation model is constructed and trained based on the deep learning platform TensorFlow. The improved particle filter constraint algorithm is used to optimize LSTM neural network which is to generate PF-LSTM prediction model. The research on health evaluation and fault prediction of complex equipment based on machine learning discussed in this paper puts forward new requirements and development direction for machine learning of complex equipment, and is of great significance for practical implementation of lean intelligent manufacturing, improvement of stability and economic benefits of complex equipment, and reduction of operation and maintenance costs.
Analysis of Computer Network Security in the Age of Large Data
Authors: Gao Lu , Yang YinGen 
Published: November 2019

Abstract
The arrival of the information age is driving the continuous development of our country’s economy and the continuous transformation of our society. At present, the number of Internet users in our country is increasing rapidly. Smartphones and portable computers have become necessary for people to travel. In the era of big data, the problem of computer network security needs our attention. Technology is a double-edged sword. While bringing convenience to all of us, it also brings a lot of problems, such as telecom fraud, information leakage and other problems about network information security caused public discussion. Therefore, this paper analyzes the problems and solutions of computer network security in the era of large data.
Research on Fault Diagnosis Technology of Thermal Test Measurement and Control System Based on Data Driven
Authors: ZeYuan Liu , Qiang Zhang , XiYuan Li , XinMing Su , Lin Zhu 
Published: November 2019

Abstract
Temperature data, as one of the most important parameters in spacecraft vacuum thermal test, is also an important characteristic parameter for fault detection of spacecraft thermal test system. From the perspective of numerical heat transfer, the physical and mathematical models of thermal test specimens are established by using finite difference method. Based on the data driving of specimen measurement temperature and heating power, the mathematical model coefficients are fitted by the combination of least squares and sliding window filtering, and then realize the on-line prediction of temperature data and fault detection of thermal test measurement and control system. Based on the thermal test data of the solar wing of a certain type of spacecraft, the above-mentioned method was verified by physical experiments. The experimental result shows that the method can accurately and effectively realize the temperature prediction not less than 2 periods, and the prediction accuracy is less than ±2°C. It can provide an effective fault detection means for the thermal test measurement and control system.
RETRACTED: Design and Implementation of Bank Wind Control Anti-fraud Project Based on Big Data Technology
Authors: Jianhao Yu 
Published: November 2019

Abstract
With the advent of the big data era, the construction process of financial big data product system is also gradually accelerating. This paper discusses a case of financial big data products created by banks-“credit risk control anti-fraud project”. The author first clarifies the requirements and background of the project construction, then shares the framework and application architecture design scheme, highlights the specific design and implementation methods of the system, finally summarizes the bottlenecks encountered in the project construction process and solutions, and points out the characteristics of the system in practical application.
2-D Scanning Galvanometer Error Analysis and Its Correction
Authors: Meng Xianyu , Cao Guohua , Li Xue , Lv Qiongying 
Published: November 2019

Abstract
The 2-D Scanning Galvanometer is an optical scanner with high precision, high reliability and high repeatability. It is also widely used. In order to eliminate the nonlinear scanning error of the 2-D Scanning Galvanometer system, in order to improve the scanning precision of the system, the theoretical distortion is derived, and the geometric distortion and various errors in the 2-D Scanning Galvanometer system are analyzed in detail. The targeted coordinate transformation method is used to correct the distortion error. The experiment proves that the method can significantly improve the scanning precision of the rapid prototyping machine.
Digital platform architecture design based on JavaEE
Authors: Li Hao , Yang Heda 
Published: November 2019

Abstract
With the comprehensive popularization of electronic technology and the internet, more and more people prefer to use smart phones, tablets and other intelligent terminal devices to read digital information on apps or Web pages, which makes the traditional publishing industry to transform to digital service. Based on the platform of JavaEE, this project provides architecture design for the transformation of rail transit publishing house to digital publishing service, integrates the traditional publishing industry resource base, and brings great help to the utilization, optimization and management of knowledge resource base.
Research on Resource Allocation Optimization of Information Management System Based on Big data Association Mining
Authors: Chen Yuyong 
Published: November 2019

Abstract
Aiming at the problem that the resource allocation accuracy of information management system in cloud computing environment is not high, the resource allocation method of information management system in cloud computing environment is improved and designed. A resource allocation algorithm of information management system is proposed based on big data association mining. The information of resource distribution in information management system is fused and reconstructed according to the key words and semantic association features, and then the feature extraction of association rules of information management system resources in cloud computing environment is carried out. The extracted association rules are used for big data training set to allocate the resources of information management system, and big data association mining and adaptive scheduling method are used for adaptive optimization control of resource allocation. The simulation results show that this method can improve the resource allocation accuracy of information management system, the anti-interference ability of resource allocation, and the efficiency of resource utilization and information management.
Simulation of gear drive design based on unity3D
Authors: Wang Yanwu , Shen Bin , Li Longteng 
Published: November 2019

Abstract
Electromechanical integration technology has developed rapidly in recent years. It is based on the information technology represented by Microelectronics technology and microcomputers. Its development is a modern industry that rapidly penetrates the traditional machinery industry and deeply integrates with electromechanical integration technology. It has mechanical technology, Microelectronics technology and information, automatic control technology, sensing and testing technology, software programming technology, interface technology and power electronics technology. With the rapid development and widespread use of computers, electromechanical integration technology has achieved advanced development and has become a system technology for computer integration, sensor detection technology, automatic control technology, and mechanical technology. And in the direction of photo-electromechanical integration, the scope of use has become larger and larger.
And we’re looking at one of the most important ones -- the gear drive, which is now the most versatile of all mechanical devices in the world. Its transmission is more accurate, more efficient, compact, reliable, and long-lasting. This time we use Solid Works and 3DS Max software to map and model gear drives and analyze their main functions.
In addition, Unity3D is also a virtual simulation software that is widely used today, and domestic talents are scarce in this area. In this context, we can try to study the design simulation of gear drive based on unity3D.
Multi-dimensional Resource Optimal Allocation Method for Service Function Chain Deployment
Authors: Hu Jingping , Pu Yojun , Li Ziyong , Wang Lisheng 
Published: November 2019

Abstract
Aiming at the problem of multi-dimensional resource service function chain deployment in a resource-limited network, a resource allocation method based on optimal weighted graph matching is proposed. The node and link resources are embodied as weights of the weighted graph, and the eigenvector decomposition of the adjacency matrix is used to get graph matching method. The simulation results show that the algorithm optimizes the bandwidth required for the deployment of the service function chain, optimizes the balance of node load and link bandwidth, supports more service requests, and has low algorithm complexity.
Fully Integrated Navigation based on INSGPS Magnetometer
Authors: Yaya Wang , Yajuan Jia , Junhui Liu 
Published: November 2019

Abstract
Inertial Navigation System (INS) and Global Positioning System (GPS) composite guidance technology are the most advanced all-weather, autonomous guidance technology. The principle is to use a Almanac filter to combine the two and after filtering, give an optimal estimate of a set of state variables (such as position, velocity, attitude angle, gyro drift, accelerometer bias, clock difference, etc.). And periodically feedback back to INS to correct it. The combined system can output all navigation information at a higher rate, superior short-term and long-term accuracy, greatly improve system availability and smoother positioning and output trajectory. The inertial navigation system (Inertial Navigation Oyster, INS) and the satellite-based positioning system represented by GPS have strong complementary advantages. GPS provides high accuracy and stability and continuously monitors the inertia sensor’s error (gyro drift).