bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accurate detection method of moving target based on sequential SAR images with multiple azimuth squint angles
Authors: Yang Shiqi , Liu Yang , Xi Peili , Li Chunsheng , Yang Wei , Zeng Hongcheng 
Published: December 2021

Abstract
In this paper, a novel moving target detection method for sequential Synthetic Aperture Radar (SAR) images with different azimuth-squint angles is proposed. In sequential SAR images, due to the movement of the target, the imaging position of moving targets among different frames differs. The method proposed in this paper uses this kind of motion characteristics to achieve the detection of moving targets in multi-frame SAR images. This algorithm can be divided into two parts: block-level detection and pixel-level detection. Block-level detection is achieved by stacked denoising autoencoders to extract the high-dimensional features of the moving target. Pixel-level detection consists of Local Binary Similarity Patterns (LBSP) coding as well as grayscale background subtraction. Pixel-level detection only needs to consider the pixels of foreground image pieces which contain moving targets. This method can not only increase the detection speed, but also suppress the false alarm problem caused by clutter. Experiments are carried out for verifying the validation of the method and the comparison are made between the proposed method and the traditional Constant False Alarm Rate (CFAR) algorithm.
Analysis of Ionospheric Disturbance on X-band SAR Image Registration
Authors: Liu Huixiang , Liu Yang , Xi Peili , Chen Jie , Yang Wei , Zeng Hongcheng 
Published: December 2021

Abstract
The atmosphere is a very important factor that affects the accuracy of X-band SAR image registration, and the ionosphere effect has the most intricate influence. In response to this problem, this paper introduces the mathematical model of ionospheric dispersion effect and scintillation effect. Then, echo simulation, imaging processing, and image registration are used to calculate the image offset caused by the ionosphere, which can determine whether the ionosphere effect needs to be compensated during image registration. Simulation experimental results show that in the X-band image registration, the dispersion effect needs to be compensated, and the impact of the scintillation effect can be ignored.
Research on Topology and Control System of High Power Grid Simulator
Authors: Li Houxiang , Zhang Yongming , Zhai Hongyu 
Published: December 2021

Abstract
Grid simulator can simulate the output of various types of power grid failure to test wind power grid-side equipment, in the design of high-power grid simulator, due to the switching frequency restrictions, will affect the control bandwidth, resulting in the power grid simulator system performance decline. Therefore, the modular multi-level converter (MMC) with higher equivalent switching frequency is used on the inverter side of the grid simulator, and the bridge arm circulation suppression strategy based on the second-order generalized integrator (SOGI) and vector scale integral (VPI) resonant controller is proposed for the internal bridge arm circulation of the converter. Finally, based on RT-LAB, the network simulator controller hardware in the ring (CHIL) experimental platform is built and experimented, the experimental results show that the design of high-power power grid simulator system can simulate the output to obtain the required grid failure.
Energy Storage Technology Used in Smart Grid
Authors: Fu Qiang , Fu Chengxi , Fu Peng , Deng Yuke 
Published: December 2021

Abstract
Energy storage is one of the main problems bothering the power system. The present research situation of energy storage is outlined. The working principles, development process and technical features of pumped storage, compressed air energy storage, flywheel energy storage, electromagnetic energy storage and chemical energy storage are described in detail. The application prospect of energy storage is proposed.
Effect of Computational Parameters on Output Properties of Underwater Explosion by Intelligent Numerical Simulation
Authors: Zheng Yonghui , Wei Jifeng , Xiao Rui 
Published: December 2021

Abstract
The computational parameters are of great influence on underwater explosion load. A one-dimensional wedge model is established to analyze the influence of boundary condition (BC), water domain and mesh density on the numerical simulation results. The results show that flowout BC is rigid boundary and transmit BC is not suitable for simulating the collapses phase of bubble pulsation. According to propagation distance of shock wave and its reflected wave, a simple method to calculate appropriate water domain is proposed. A positive correlation between mesh density (λ) and calculated peak pressure of shock wave (P
m) is found. When λ tends to infinity, simulated Pm in near field is quite reliable, but the values in relatively far field are lower than empirical results.
Application of Recommendation Algorithm and Big Data Technology in Computer English Corpus Database
Authors: Wang Qingling 
Published: December 2021

Abstract
Corpus linguistics is one of the branches of modern linguistics. It is also a cutting-edge research problem of modern linguistics spawned by the background of the information technology era. Corpus linguistics can reset the teaching role of English majors. College English vocabulary teaching makes use of the corpus’s abundant real language materials and the computer’s ability to analyze and process large-scale corpus, guide students to explore the usage of vocabulary in the context, and use the corpus to deepen students’ autonomous learning. This article will start with the development of corpus linguistics, explain the necessity of combining corpus and English language teaching, and propose a corpus linguistics-based English language learning platform system in colleges and universities, and combine with semantic recommendation algorithms to verify that the system is in English learning.
The Computer Intelligent Selection of Scientific Research Subjects Through Ensemble Learning for Large-Scale Data Sources and Deep Neural Network
Authors: Ma Yan , Zou Lida , Liu Ke , Han Yingkun , Ma Lei 
Published: December 2021

Abstract
Selecting a proper scientific research subject is critical for scientific researchers and managers. Scientific researching data are from massive sources and have various attributes. For the problem of subject selection, feature extraction and prediction model play important role in performance optimization. In the paper we introduce ensemble learning method to help find the best fit attributes describing data. Our ensemble learning models include random forests, support vector machine, Boltzmann machine and decision tree. Since the data are from many data sources, we adopt multiple models of deep neural network. An acceleration method is used to reduce the training time as well. Experiments shows that the proposed approach performs better than RNN algorithm both in accuracy ratio and recall ratio. The model selection module and acceleration method help optimize the time cost largely.
Research on Dynamic Knowledge Map Service System Using Computer Big Data
Authors: Zhang Nan , Zhang Wenqiang , Shang Yingnan 
Published: December 2021

Abstract
The emergence of computer big data related data provides a new method for the construction of knowledge links in the knowledge map. This realizes an objective knowledge network with practical significance that is easier to be understood by machines. The article combines the four principles of linked data publishing content objects and their semantic characteristics, and uses the RDF data model to convert unstructured data on the Internet and structured data that adopts different standards into unified standard structured data for association. The system forms a huge knowledge map with semantics, intelligence, and dynamics.
Mathematical Multivariate Statistical Analysis and Big Data Information System Establishment on Urban Pollutant through Intelligent Computational Method
Authors: Guo Mingyue 
Published: December 2021

Abstract
This paper mainly adopts the actor analysis and correspondence analysis in multivariate statistical analysis., for 6 indicators of the main pollutant contents emission in waste gas by 31 cities in China. The results of Factor Analysis showed that with cities in 6 indicators were controlled by two independent factors. The 2 factors, which were determined and named, were f
1, domestic waste gas pollutant emission factor, and f
2, factor of Industrial waste gas pollutant emission. The percentage of accumulated contribution of variances was 85.51%. The result s of correspondence analysis showed that, six indicators and cities which had significant or close relationships with 31 cities.
Dynamic Response Measurement of Elastic Structures Through Smart Digital Image Correlation Method and Calculation
Authors: Wang Jiawen , Liang Jingqi , Wang Rui , Qi Xiaobin 
Published: December 2021

Abstract
Based on the proper improvement and development of the basic principle of digital image correlation method, a 3D-DIC measurement system is built. Its measurement principle and application process can be divided into four steps: camera calibration, image acquisition, image matching and three-dimensional reconstruction. The measurement system can realize non-contact and full-field measurement and has the advantages of not being affected by the test environment, not imposing additional mass on the test model, and high signal-to-noise ratio of the measured data. Taking the elastic plate model as an example, the accuracy and reliability of the measurement system in the measurement of static and dynamic displacement response are verified by using the measurement results of laser displacement meter as a reference. In addition, the tensile strain of the projectile aluminum alloy tail model and the ground mode of the elastic plate are tested to further verify the practicability of the test system.
Computer Assisted Chord Detection Using Deep Learning and YOLOV4 Neural Network Model
Authors: Ru Yingdong 
Published: December 2021

Abstract
Music symbol recognition is an important part of Optical Music Recognition (OMR), Chord recognition is one of the most important research contents in the field of music information retrieval. It plays an important role in information processing, music structure analysis, and recommendation systems. Aiming at the problem of low chord recognition accuracy in the OMR recognition model, the article proposes a chord recognition method based on the YOLOV4 neural network model. First, the YOLOV4 network model is used to train single-voice scores to obtain the best training model. Then, the scores containing chords are trained through neural network fine-tuning technology. The experimental results show that the method recognizes the chords with great results, the model was tested on the test set generated by MuseScore. The experimental results show that the accuracy of note recognition is high, which can reach the accuracy of duration value of 0.96 which is higher than the accuracy of note recognition of other score recognition models.
Research on the peak prediction and intelligent computing of reversal error for the tilt feed system installed on the precision machine tools
Authors: Feng Bin , Yang ManZhi , Dang Meng 
Published: December 2021

Abstract
To effectively predict the peak of reversal error of tilt feed system and reduce reversal error caused by friction and gravity components, a peak prediction method of reversal error for tilt feed system on the precision NC machine tool is proposed. According to the load, tilt angle, motion trajectory, maximum static friction torque and relevant dynamic characteristic information, the peak prediction formula of the reversal error for the tilt feed system is established by mathematical derivation based on the kinematics, dynamics and torque balance during the process of reversal. Thus, the peak of reversal error for the tilt feed system can be obtained. The experimental results show that this method can achieve a good prediction effect, and can predict the peak of reversal error before the machining. It provides a theoretical basis for the reversal error suppression.
Study on a visual coder acceleration algorithm for image classification applying dynamic scaling training techniques
Authors: Huo Junliang , Ling Jiankun 
Published: December 2021

Abstract
Nowadays, image classification techniques are used in the field of autonomous vehicles, and Convolutional Neural Network (CNN) is used extensively, and Vision Transformer (ViT) networks are used instead of deep convolutional networks in order to compress the network size and improve the model accuracy. The ViT network is used to replace the deep convolutional network. Since training ViT requires a large dataset to have sufficient accuracy, a variant of ViT, Data-Efficient Image Transformers (DEIT), is used in this paper. In addition, in order to greatly reduce the computing memory and shorten the computing time in practical use, the network is flexibly scaled in size and training speed by both adaptive width and adaptive depth. In this paper, we introduce DEIT, width adaptive techniques and depth adaptive techniques and combine them to be applied to image classification examples. Experiments are conducted on the Cifar100 dataset, and the experiments demonstrate the superiority of the algorithm on image classification scenarios.
Grasp Detection Based on Light-Weight Hierarchical Fusion Convolutional Neural Network
Authors: Xu Ziang 
Published: December 2021

Abstract
This paper presents a light-weight Hierarchical Fusion Convolutional Neural Network (HF-CNN) which can be used for grasping detection. The network mainly employs residual structures, atrous spatial pyramid pooling (ASPP) and coding-decoding based feature fusion. Compared with the usual grasping detection, the network in this paper greatly improves the robustness and generalizability on detecting tasks by extensively extracting feature information of the images. In our test with the Cornell University dataset, we achieve 85% accuracy when detecting the unknown objects.
Application of Machine Vision Recognition System in Mobile Robot
Authors: Fu Yan 
Published: December 2021

Abstract
In order to solve the problem of autonomous recognition of hexapod robot and realize the intelligent and humanized development of robot, OpenMV is taken as the main platform, hexapod robot is taken as the main machine carrier, Python is taken as the main development language, C language is taken as the auxiliary development language, and the reasonable application of image processing technology is added. A simple visual recognition system based on OpenMV is designed to realize the application of visual recognition.
Intelligent Calculation Method and Algorithm Optimization of Consumption Reducing for SCR and SNCR in Denitriation System
Authors: Li Kaiqiang , Chen Xiaobing 
Published: December 2021

Abstract
Since the environmental laws have been more and more strict about pollutants emissions of thermal power plant, SNCR and SCR system seems to be typical configuration. However the ammonia consumption calculation differs from different designers. In this paper the popular and experienced calculation method is introduced for thermal power plant dinitriation system.
Research on Character Computer Intelligent Recognition of Automobile Front Bar Bracket Based on Machine Vision
Authors: Shen Chuanxing , Zhu Yongjian , Wang Chi 
Published: December 2021

Abstract
Aiming at the problem that the characters in the superimposed character area on the surface of the front bumper bracket of the car are difficult to recognize, a machine vision-based automobile front bumper bracket character recognition method is proposed, use Python, OpenCV and Halcon computer vision library for software system design. For eight images collected from different angles of light source directions, an innovative method of polynomial fitting gray value can reducethe uneven illumination of the images. The photometric stereo algorithm is used to obtain a high-contrast character image, and the separation of the two types of characters is simultaneously achieved. After the image filtering, opening and closing processes canremove background interference, use a morphological improvement algorithm based on scanning algorithm to complete character positioning, and then the improved algorithm of projection dichotomy based on the connected domain size is used to complete the character segmentation, and finally the support vector machine is used for character feature recognition. The experimental results claim that these methods can quickly separate superimposed characters and complete the recognition of non-color difference convex characters and inkjet characters, and the average recognition accuracy rate is over 96%, which meets the expected recognition requirements.
A Novel Back Propagation Neural Network Optimized by Rough Set and Particle Swarm Algorithm for Remanufacturing Service Provider Classification and Selection
Authors: Cao Jianhua , Xia Xuhui , Wang Lei , Liu Xiang , Zhang Zelin 
Published: December 2021

Abstract
Aiming at the problem that the high classification feature dimensionality of the back propagation neural network (BPNN) leads to slow convergence speed and the initial weight and threshold sensitivity of the BPNN lead to the problem of easy convergence to the local optimum. A novel BPNN optimized by rough set and particle swarm algorithm (RS-PSO-BPNN) for remanufacturing service provider classification and selection is proposed. First, the attribute reduction method of rough set theory is used to preprocess the classification features of remanufacturing service providers, redundant attributes are deleted from the decision table, and the input feature dimension is reduced; then the PSO algorithm is used to optimize the network Initial weight and threshold. Finally, the proposed method is used for the selection and optimization of remanufacturing service providers. The results show that the proposed RS-PSO-BPNN has higher classification accuracy and efficiency for the problem, which provides scientific decision supports for remanufacturing service provider selection.
Research on Airborne Electromagnetic Whole-area Apparent Resistivity Imaging Algorithm in the Detection of Goaf in Rail Transit
Authors: Jia Yajuan , Zheng Jianbo , Zhou Hongfang 
Published: December 2021

Abstract
Depth apparent resistivity imaging is an important process of data processing and analysis in the aviation transient electromagnetic method. It can provide reference value of conductor depth, vertical extension, and other information, and can accurately provide the measurement of each aviation transient electromagnetic measurement system. The structural section of the apparent conductivity of the one-dimensional layered medium on the line. As an advanced geophysical exploration technology, the aerial transient electromagnetic method has been applied significantly in the exploration of polymetallic minerals abroad in recent years. In this paper, based on the theory of ground-to-air transient electromagnetic method with multiple radiation sources, a corresponding multi-component global apparent resistivity definition method is established. The advantages of using the magnetic field strength to define the global apparent resistivity of the multi-radiation field source ground-air system are analysed. For each component of the magnetic field strength, respective global apparent resistivity algorithms are proposed to realize the multi-component, full-time, and full-space visual resistivity. The resistivity is calculated, and the influence of the offset on the global apparent resistivity is analysed. By adjusting the relative position of the source and the current direction and other parameters, the multi-radiation source transient electromagnetic ground-air system can not only strengthen the signal strength of different components, weaken random interference, but also better distinguish the location of underground anomalies
Research on Aiming and Tracking Ability Evaluation Model of Laser-guided Missile
Authors: Li Wuzhou , Lin Shicong , Liu Zehui 
Published: December 2021

Abstract
Laser guidance is a continuous process, during which a lot of operational data and corresponding changes are generated. Aiming at the live-fire drill shooting training and assessment evaluation of a certain type of laser-guided missile, this paper analyzes the principle of laser guidance, studies the weighting system of the information field based on the importance of characteristic intervals, integrates and processes the data and outputs the aiming effect picture and sheet, and constructs the aiming and tracking ability evaluation model. Stationary targets and moving targets are tested respectively, and the verification model can realize the calculation of laser information field, the integration of coordinate data, the output of aiming effect diagram, and the evaluation of the shooter’s aiming and tracking ability expressed in a percentage system. The model realizes data collection, integration and processing based on MATLAB software, and builds graph and table output ports, which can realize low delay processing of a large amount of short-term data.
Design of computer network security storage system based on cloud computing technology
Authors: Wang Xi , Shi Lei 
Published: December 2021

Abstract
Nowadays, computer networks are widely used. Besides the convenience, people also feel the pressure of network viruses and hackers. At present, in the process of fighting against network security threats, traditional defense measures have been difficult to meet. Therefore, in order to meet the needs of computer network security under cloud computing technology, and for the future of cloud computing high-quality network storage system can be gradually optimized. In this paper, from the cloud computing and network security storage system two aspects of the computer network security storage performance analysis and design; Then from the design framework, through careful design, to ensure the reliability and security of data upload, storage.
Research and Application of Cloud Computing and Big Data Technology
Authors: Hui Bingran 
Published: December 2021

Abstract
The combined application of new network technologies with the traditional Internet and industry has enabled new businesses such as the Internet of Things and cloud computing to be gradually derived. To realize the construction of a modern society in China, great attention should be paid to the application of new network technologies such as cloud computing in actual work. Now through data integration and correlation analysis, the accuracy and innovation of information are further realized, and the overall work efficiency and work quality are improved. This paper systematically analyzes the network big data technology and applications of cloud computing and Internet of Things, and conducts research from data collection, data integration, data processing, and data governance. Its main purpose is to make the value and function of these new network technologies fully utilized