bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of numerical calculation to simulation analysis of stray current corrosion in radiator of HVDC Converter Valve
Authors: Cui Pengfei , Guan Shengli , Guo Jianbao , Wang Guangwu , Wang Haijun , Chen Jianye , Huang Kefeng , Geng Man , Yang Weiwong , Deng Longlong 
Published: March 2020

Abstract
According to the electrical structure design of the converter valve, the stray current scattered in the water path of the converter valve tower is coupled in the internal waterway of the aluminum radiator. It promotes the occurrence of corrosion on the inner surface of the waterway. However, due to the wide range of stray current in the valve tower distribution pipe, many factors affecting the properties of fluid and the actual engineering not allowed to be measured, and so on. For a long time, the corrosion of converter valve radiator has always been an urgent technical problem to be solved in the field of DC transmission. It has received extensive attention in various research institutions around the world. In this paper, according to the distribution pipe of 500kV light-triggered converter valve tower, the structure model and equivalent circuit model of the water path are built and the mechanism of stray current corrosion of aluminum radiator is analyzed. At the same time, the circuit simulation software is used, and according to Faraday’s law, the stray current, corrosion rate and annual corrosion rate of aluminum radiator in distribution pipe were calculated quantitatively. According to the numerical calculation results, the corrosion of aluminum radiator is compared with that of actual engineering aluminum radiator, the primary and secondary relationship of stray current corrosion in aluminum radiator corrosion is put forward, which provides the basis and reference for system optimization and engineering transformation.
Research on Calibration System of Low Frequency Electric Field Measuring Instrument
Authors: Zuo Jiansheng , Zheng Xiaonan 
Published: March 2020

Abstract
In order to calibrate various types of low frequency electric field measuring instruments, this paper constructs a calibration system by using the voltage amplifier. This system uses a function signal generator to output voltage signal, which is amplified by a voltage amplifier and loaded onto the plate to generate an electric field. The intensity of electric field is achieved by adjusting the distance between the two electric plates and the output multiple of the amplifier. The electric field generated by the system ranges from 30Hz to 100kHz in frequency and the field intensity ranges from 1V/m to 3kV/m. The system established in this paper is easy to operate and use, which greatly reduces the difficulty of generating continuous low-frequency electric fields in conventional calibration systems. The experimental results show that the feasibility, stability and reliability of the proposed system are superior to the traditional high-voltage source and TEM (Transverse Electric and Magnetic Field) chamber method.
Traffic Prediction Model Based on Improved Quantum Particle Swarm Algorithm in Wireless Network
Authors: Yu Yao , Liu Shumei , Wang Linlin , Teng Fei , Li Shuang 
Published: March 2020

Abstract
With the rapid development of data transmission, how to achieve fast and accurate prediction of wireless network traffic is an important issue to data collection. In this paper, we propose a traffic prediction model, namely improved quantum particle swarm optimization based on BP neural network. We adopt BP neural network as the basic architecture. On this basis, wavelet multi-resolution analysis technology is introduced as the pre-processing method for prediction model input. Aiming at the shortcomings of slow convergence and easy falling into local optimum of BP neural network, the improved quantum particle swarm algorithm is used to optimize it. Simulation experiments show that compared with the traditional algorithms, the proposed model has higher prediction accuracy and faster convergence rate.
FPGA virtual platform based on systemc and verilog
Authors: Song Chunwei , Wu Xianghu , Tao Yongchao 
Published: March 2020

Abstract
With the development of science and technology, FPGA is widely used in many fields of society, such as communication, algorithm implementation, and embedded field, with its flexible programming, high-performance parallel computing capabilities, and short development cycles.There are already many types of FPGA hardware products on the market, but the tedious problems of the hardware system itself and the cost problems of the hardware system bring a lot of inconvenience to users’ learning, which is inconvenient for users to learn, develop, and debug FPGA jobs.This article is based on such a problem. From the software level, a scheme for implementing an FPGA virtual platform based on systemc and verilog is proposed.This solution uses software to simulate the implementation of FPGA hardware, TCP communication technology, systemc and verilog applications to meet the needs of users to learn, develop and debug fpga without a hardware platform.
The Detection Algorithm of Broken Wires in Power Lines Based on Grabcut Segmentation
Authors: Guo Jiayi , Guo Xuelin , Wang Limin 
Published: March 2020

Abstract
Unmanned aerial vehicle (uav) has the advantage of rapid and efficient in power line fault detection. However, the background of power line image taken by uav is complex, and it is difficult to extract the foreground power line for fault detection.Aiming at this problem, we propose an algorithm based on Meanshift superpixel segmentation and Grabcut for further segmentation optimization. At the same time, the RANSAC algorithm is used for denoising to extract accurate power lines. Finally, the power lines are constrained based on their slender characteristics. Compare the width change on the same wire to detect the occurrence of strand breakage in the power line. The experimental results show that the algorithm has a good effect on the detection of broken strands in power lines.
Automatic Segmentation of Microaneurysm in Retinal Fundus Images
Authors: Zhang Guodong , Suraweera HasinduMaduranga , Pratama AdevianFairuz , Zhao Dazhe , Gong Zhaoxuan , Guo Wei 
Published: March 2020

Abstract
Diabetes interferes with the body’s ability to use and store sugar (glucose), which it can cause damage throughout the body. Generally, Microaneurysm (MA) is the first clinically observable lesions in the Diabetic Retinopathy (DR). Since interference of biological objects in eye fundus images are similar to MA, locate Microaneurysm lesions precisely is a challenging task for researches. This research employs U-Net Convolutional Neural Network to model saliency of objects in images, global and local context are both taken into account to provide a better initialization for the process. Feature extraction techniques are applied first to assign a local saliency value to each pixel by considering its local context from fundus images such as ORB, SURF, and MSER. The extracted feature vectors are applied for training the network. The sum of the weighted salient object regions produce the final saliency map, then implements U-Net to segment MA lesions.Our experiment has carried out using the publicly available Indian Diabetic Retinopathy Image Dataset (IDRiD), which has used in "Diabetic Retinopathy: Segmentation and Grading Challenge" workshop and our proposed method has given an outstanding accuracy of 98.78 %.
An Improved Random Forest Model Applied to Point Cloud Classification
Authors: Xue Doudou , Cheng Yinglei , Shi Xiaosong , Fei Yan , Wen Pei 
Published: March 2020

Abstract
Urban laser radar point cloud building extraction is a hot spot in recent years, but the accurate distinction between vegetation, buildings and man-made objects has always been a difficult point. In this paper, a point cloud classification algorithm based on ICSF and weakly correlated random forest are proposed for the problem of low classification accuracy. Firstly, the data is ground-filtered by ICSF algorithm, then the decision tree is constructed, and correlation analysis is performed based on the maximum mutual information coefficient. A decision tree with the smallest correlation coefficient and the highest precision is selected to form a random forest. Finally, the decision results are weighted and completed. Point cloud classification. This paper validates the model through the Vaihingen city dataset and ranks the importance of the features according to the method of reducing the average precision. Compared with the traditional random forest classification algorithm, the classification accuracy is improved by 4.2%, which shortens the model time.
Atmospheric Pollutant Prediction Based on Wavelet Decomposition and Long Short-Term Memory Network
Authors: Li Lei , He Zhexiang 
Published: March 2020

Abstract
To solve the problem that current atmospheric pollutant prediction research only pays attention to one pollutant type, a Long Short-term Memory Network (LSTM) atmospheric pollutant prediction model based on Wavelet Decomposition (WD) is proposed, predicting daily average PM2.5, PM10, SO2, NO2 and O3 concentration for the next day. Based on data collected from the national control station (NO.1338A) in Changsha from 2015 to 2018, the model was verified. The results show that for the prediction of atmospheric pollutants, compared with the LSTM prediction model established directly with the original atmospheric pollutants, the root mean square error and the absolute average error of the hybrid model are lower, and the accuracy of the level prediction is higher.
A review of military knowledge models
Authors: Wang Hongyu , Yang Zhaohong , Zhou Yuwei 
Published: March 2020

Abstract
In the process of operation command, the commanders often face a mass of battlefield information information, which mixed with many useless or even false information, This will seriously affect the commander’s timely decision-making, and would not meet the requirements of "agile command" for future operations, By constructing military knowledge model, the fault problem of command information system can be eliminated,and finally, the commander will change from "information" to "knowledge".This paper introduces the main contents and related technologies of military knowledge model, and summarizes the development and challenges of military knowledge model.
End-to-End Image Super-Resolution via Generative Adversarial Network
Authors: Li Xu , You Xiangdong 
Published: March 2020

Abstract
Image super-resolution is to extract information from single or more low- resolution images and use this information to get the corresponding high-resolution image. The ability to capture high texture details of low-resolution images is one of the impressive advantages of generative adversarial networks (GANs). This paper mainly studies the image reconstruction method based on a single low-resolution image, and builds an end-to-end image Super-Resolution method via generative adversarial networks to improve the image resolution. Convolution neural networks use the mean square error (MSE) as loss function. For such loss function, structural similarity (SSIM) and peak signal to noise ratio (PSNR) can be obtained, but high texture details of original images are often lost. The generative adversarial network mainly contains two parts: generator network and discriminator network. Adversarial loss uses a discriminator network to push our solution to a natural image manifold, which is trained to distinguish super-resolution images from photo-realistic images. Additionally, content loss of our network was indicated by perceptual similarity rather than similarity of pixel-wised space.
Detecting Bank False Account Based on Naive Bayesian Network
Authors: Wan Weiqing , Zeng Qingyan , Wen Zhicheng 
Published: March 2020

Abstract
With the development of Internet, more and more fraudulent cases are committed on the basis of bank false accounts, but the means and ability of bank to verify the true identity of customers are limited. Generally, a bank online verification system can only check the authenticity of identity cards through inquiry, while the verification of people and cards can only be completed manually by Bank tellers, but only by the counter. Personnel’s naked eye judgment is prone to potential risks. Aiming at the problem of fraud of bank customer identity, this paper proposes a method of automatic detecting bank account fraud using Naive Bayesian Network classification model, and verifies the feasibility of this method through specific examples.
Chestnuts quality online detection technology based on acoustics
Authors: He Wenbin , Wei Aiyun , Ming Wuyi , Jia Haojie , Chen Zhijun , Wu Ziqiang 
Published: March 2020

Abstract
In order to overcome the influence of various uncertain factors on the quality of chestnuts in the process of manual detection, an online detection method based on acoustics was proposed. In this paper, a multilevel inspection platform for chestnut surface defects based on acoustics is constructed to realize real-time acquisition and measurement of chestnut surface defects. Through acoustic detection, the sound collected from the feature area is converted into a time-frequency map, and image processing was performed using matlab software. At the same time, in the return air detection, the chestnuts with wormholes will produce a return air, which was used as the secondary detection of the chestnuts. This study provides a useful reference for chestnuts quality testing and improves the objectivity and accuracy of chestnuts quality testing.
Construction of Robotic Virtual Laboratory System Based on Unity3D
Authors: Lin Mu , San Lijun , Ding Yu 
Published: March 2020

Abstract
Paper introduces the virtual reality laboratory system based on virtual reality technology. Using the Unity3D engine, the robotic arm is used as the research object to develop the virtual simulation teaching system. The system is released on the PC side and applied to the teaching and training of the mechanical arm structure. Establish a virtual laboratory to realize the first person perspective roaming, on-the-spot observation robot arm structure, assembly virtual interaction simulation, intelligent automatic assembly simulation, solve complex mechanical structure cognitive teaching and assembly planning problems. The application of powerful virtual reality technology to engineering technology teaching has improved the teaching effect and teaching efficiency of mechanical structure cognition, structural assembly training, curriculum design and other teaching links.
A File cloud sharing method based on XOR operation
Authors: He Yue , Xia Gao , He Chengwan 
Published: March 2020

Abstract
To realize the secure storage and sharing of files, a file cloud storage method based on XOR (Exclusive OR) operation is proposed. In this method, a file is encrypted into multiple file slices based on XOR operation, then these file slices are uploaded to different locations of the public cloud platform. These file slices can be downloaded from the cloud platform and restored to the original data file based on XOR operation. In this way, even if a file slice is stolen, the information of the file will not be stolen. This paper gives the XOR-based encryption and restoration algorithms for the two cases where the original data file is not divided or divided into arbitrary secrets. Finally, we give the implementation method of the prototype.
An adaptive RBF hidden layer generation algorithm
Authors: Chen Haibo , Zhang Chenyu , Hu Gao 
Published: March 2020

Abstract
We presents an adaptive learning algorithm of RBF neural network. This algorithm uses an adaptive splitting operation based on network sensitivity and sample density to dynamically change the number of nodes in the hidden layer of RBF network. At the same time, a refactoring operation based on energy consumption is proposed, and the connection weights of the hidden layer and the output layer are obtained by using the least square method. In the experiment of iris sample classification, the recognition rate of the model is 95%.
Logic Computing in Memristor-based neural networks
Authors: Bu Kai , Liu Haijun , Wang Wei 
Published: March 2020

Abstract
This paper presents a neural network based on a memristor crossbar to realize linearly separable logic functions with a single-layer perceptron in just one step, while realizing linearly nonseparable functions with multilayer neural network in multiple steps, such as addition operation and XOR logic. We could implement different logic functions in the same crossbar and circuit by adjusting the resistance of the memristor. It’s conveniently changed by analog tuning of the applied voltages. As the neural networks are capable of tolerating the random diversity effects of device variations and noises to some degree, the proposed method can tolerate the variations of crossbar arrays. We also built a verification platform with Xilinx FPGA, Analog-Digital converters, and a 32x32 memristor crossbar array to show how the logic function works.
The Algorithm of Scene Summary synthesis Based on Semantics
Authors: Zhu Yaling , Wei Xiaodi , Li Xiangwei , Zheng Gang , Wang Juan 
Published: March 2020

Abstract
According to the key technique of semantic-based video retrieval, the paper presents directly extracting I-frames in the compressed domain, and using the DC coefficient of I frame to establish the model of information system. Then the reduction of I Frame by the theory of attribute reduction based on RS, to produce A key frame that reflects the main content of the lens. Finally, the key frames ‘s DC coefficient has constructed the mathematical modeling, and has classified the shot according to the differences of frames. So the polymerization of I frames of similar lens, we could obtain the scenario summary.
ECG diagnosis based on one-dimensional convolutional neural network
Authors: Mei Qianwen , Liu Tangyou 
Published: March 2020

Abstract
At present, cardiovascular disease has become the "first killer" endangering human life and health. Arrhythmia is one of the common diseases and frequently-occurring diseases. It is one of the main causes of death, and it is often accompanied by many complications. It can cause serious heart disease or even death, so it is very important to find a timely diagnosis of arrhythmia [1]. This article is based on the ECG data which is obtained from long-term monitoring to perform classification diagnosis on the occasional irregular heartbeat. It can be summarized as a series of features extraction and automatic classification diagnosis after a series of preliminary processing of the obtained long-term ECG data. This method is based on one-dimensional convolutional neural network to classify the normal heart rhythm and five abnormal heart rhythms according to the single lead data.
Impact of digital technology and internet to film industry
Authors: Fang Jingxuan , Xiong Wei 
Published: March 2020

Abstract
With computer technology development, film production changes a lot. The film new technical revolution has been brought because of the digital technology application. The film art development also has been injected new vitality. The film industry is restructured and developed with huge room because of integration of internet and film industry. The combination of internet and technology can play an important part in relevant industry.
Research on Factors Affecting Campus Bullying Based on Factor Analysis Model
Authors: Ruan Dexiang , Hu Baokun 
Published: March 2020

Abstract
School bullying has frequently appeared in major primary and secondary schools and even universities. It has become a topic of great concern to all sectors of society. Campus safety issues have greatly affected the physical and mental health of young people, and even have an indelible shadow on their future. The rapid development of technology has provided a powerful help for studying the factors affecting contemporary students’ school bullying. This paper uses the factor analysis model to compare the dominant factors affecting school bullying from the horizontal aspect, which can provide reference for parents and schools to judge potential campus bullying, and has positive significance for preventing and controlling campus bullying.
Research on Software Runtime Credibility Based on Cluster Analysis
Authors: Wan Weiqing , Zeng Qingyan , Wen Zhicheng 
Published: March 2020

Abstract
Monitoring and analyzing the reliability of software runtime behavior in an open, dynamic, and complex network environment is very important for modern distributed software. The characteristics of the extrinsic representation in the execution of distributed software are collected, and the data is executed based on the history of the software, and the wide square cube cluster is constructed, and the reliability is analyzed, and the fault tolerance processing and the fault diagnosis of the succeeding system are analyzed. Performance optimization is facilitated. Simulation experiments show that the proposed method has a unique advantage in comparison with other methods in reliability analysis of software behavior.
Dynamic stability analysis and Experiment of the Mobile Elevating Work Platforms
Authors: Gao Xuhong , Sun Di , Ge Yan , Yang Xiantao , Li Jin 
Published: March 2020

Abstract
In order to calculate dynamic stability of the Mobile Elevating Work Platforms (MEWP) during driving across obstacles and operating on the slope, the analysis method based on mathematical models and simulation models of the MEWP was put forward, then the MEWP dynamic simulation models where the tires and booms were set as flexible bodies were made by ADAMS. Four kinds of the most dangerous working conditions were analyzed, and then the dynamic curves of tire loads and dynamic stability safety coefficient were obtained. Next, the dynamic stability of the MEWP during operating on the slope was verified by field experiments, and the results show that the minimum dynamic stability safety coefficient is 0.157. By comparison and analysis of dynamic stability safety coefficient between simulation analysis and field experiment, it can be seen that the deviations are all positive and less than 5%, therefore the results show that the dynamic stability of the MEWP during driving across obstacles are satisfied. At the same time, the analysis method combining dynamic simulation and some field experiments of the MEWP is proved reliability, accuracy and economy. In all, the dynamic stability of the MEWP can be calculated accurately by ADAMS, in the meantime, the overload operation test (1.5 times rated load) must be performed on the 5° cross slope before the MEWP are selled, and so long as the minimum dynamic stability safety coefficient is no less than 0.1, the MEWP are proved stability and safety.
Fluid-Structure Coupling Vibration of an Elastically Restrained Circular Plate
Authors: Yao Shihui , Fu Aihua , Xing Junhua , Zhou Jinhua , Tian Bo 
Published: March 2020

Abstract
In this paper, an analytical method is presented to calculate the fluid-structure coupling vibration of an elastically restrained circular plate. The displacement of the plate is expanded in series of dry mode shapes and the motion of the fluid is described by velocity potential function. Considering the equilibrium differential equation of the plate and the velocity continuous conditions at the interface of fluid and structure, the Galerkin method and expansion of Fourier-Bessel series are used to establish the governing equations of the system. The free vibration characteristics of the circular plate in contact with fluid are studied and the present method is verified by comparison with numerical results. Furthermore, the influence of spring stiffness and fluid depth on the free vibration characteristics of the circular plate is investigated.