bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Responsive Information Interaction Design Based on Facial Recognition Technology
Authors: Ma Chen 
Published: June 2021

Abstract
The recognition and tracking down moving figures have always been a hot research topic in the field of computer vision. With the development of artificial intelligence in recent years, the application of deep neural networks involves various fields. Compared with the previous solutions, some problems even have better solutions based on deep neural networks. This article mainly discusses targeting people based on face recognition and realizing movement tracking down the target people. The research content of this article can be divided into two aspects. The first aspect is the identification of the target person. Since the overall characteristics of the target individual are not recognizable, and the overall recognition easily leads to errors, this article uses the method of identifying the target person based on the face. This article introduces the application of deep neural networks of facial recognition, and finally selects the SeetaFace faces recognition system that does not depend on any third-party library functions and is open source. The second aspect are the tracking of sports figures. When the algorithm recognizes that the similarity between the image and the target face reaches a certain level, it will automatically switch to tracking the overall torso of the moving person. In the tracking part, a variety of algorithms are compared and compared with experimental data. Experiments have verified that the convergence of face recognition and tracking algorithms is feasible, and this solution not only prevents the interference and occlusion of similar colored objects of the tracking process, but also ensures the stability of the tracking process and is robust., Accuracy and real-time performance have also achieved good results.
RETRACTED: Defect detection of solar cell based on data augmentation
Authors: Wang Yunyan , Luo Shuai , Wu Huaxuan 
Published: June 2021

Abstract
In this paper, a true and false data fusion algorithm based on deep convolution confrontation generation network and random image Mosaic is proposed, which improves the training data volume by 800 times. At the same time, the network model is optimized with light weight to reduce model training parameters, storage space and prediction time. The experimental results show that compared with the original data set training and the model training using the traditional data enhancement algorithm, the test accuracy of the model training obtained by the fusion of real and false data is improved by 30% and 17% respectively, reaching nearly 77%. After the lightweight treatment, the model parameters were reduced to about 1/2 before the treatment, and the test time for each image was shortened from 57ms to 22ms. The research shows that the fusion algorithm can effectively help the image classification task with insufficient data to alleviate the problems of network overfitting and poor model performance. The lightweight optimization model can not only ensure the accuracy, but also compress the size of the model to speed up the testing speed. It is helpful to promote the application of intelligent algorithm in industrial production so as to save cost and improve production efficiency.
Study on Image Classification Algorithm Based on Improved DenseNet
Authors: Wang Gaihua , Guo Zhao , Wan Xizhou , Zheng Xu 
Published: June 2021

Abstract
In order to perform fast analysis and recognition of power equipment images. A residual attention mechanism based image classification model is proposed. First, the power equipment dataset is preprocessed and the power equipment dataset is randomly divided into a training set and a test set. Based on the improved DenseNet, the image features of the training set are extracted. The training set images are used for training, and the classification test is implemented on the test set. The experimental results show that the accuracy of the improved DenseNet algorithm is improved by 8.89%. It can be seen that the classification accuracy of the improved DenseNet algorithm is effectively improved.
Research on High Precision of Projection Exposure Imaging of Large-Scale Integrated Circuit Lithography Machine
Authors: Zhang Minshan 
Published: June 2021

Abstract
The precision and work efficiency of the distributed repeat projection lithography machine (DSW) directly affect the special requirements of the large-scale integrated circuit production process. In order to improve the exposure and imaging accuracy of the large-scale integrated circuit lithography machine projection, the paper proposes the lithography machine projection object image difference in-situ detection (AMF) technology based on particle swarm optimization. The article analyzes the basic principles of this technology using special test marks to detect spherical aberration, astigmatism, and coma of the projection objective, and discusses the method of using the alignment position coordinates to calculate the imaging position shift caused by aberration. The experimental results show that the AMF technology can achieve accurate measurement of aberration parameters such as spherical aberration, coma, and astigmatism.
Point cloud target detection and tracking algorithm based on K-means and Kalman
Authors: Fang Liangyu , Luo Qian , Sun Yue 
Published: June 2021

Abstract
This paper is mainly about point cloud image and data processing, virtualization and visualization. An improved K-means iterative clustering algorithm is proposed. In addition, cloth simulation filtering algorithm is used to extract road surface information. On this basis, bilateral filtering and statistic filtering are used to smooth image denoising. Kalman filter is used to track and predict point cloud targets. Using the variance of innovation sequence, the noise variance and observation noise variance can be estimated and corrected gradually in the process of system calculation. The error is relatively small, and the accuracy and reliability are improved. For disordered, occluded, sparsity and noisy, complex terrain image, how to improve the denoising effect, the accuracy and effectiveness of target extraction and tracking is the focus of this paper.
Classification of pulmonary lesions based on CNN and chest X-ray images
Authors: Xue Wen 
Published: June 2021

Abstract
This article mainly introduces the convolutional neural network (CNN) and uses CNN to realize the processing and classification prediction of chest X-ray images (CXR), to determine whether the lung has lesions, and finally the final AUC score of 0.85556 through CNN. In order to further improve the accuracy, after referring to many documents and considering the actual situation, I chose to perform principal component analysis (PCA) in the image preprocessing part, replace the random initial sample with the principal component initial sample, and replace the random initial kernel with the principal component initial kernel. To avoid staying in the local optimum when stochastic gradient descent finds the optimal kernel. The PCA + CNN model predicted an AUC score of 0.89333, an increase of 0.03777.
An Adaptive Recovering Algorithm for The Color Bar Code Image Based on Gamma Transforms
Authors: Niu Wanhong , He Jielin , Liu Xingchao , Huang Lingxiao , Zhao Xiaojuan 
Published: June 2021

Abstract
Aiming at the problem of error reading caused by color deviation under abnormal light source, a color bar code image restoration algorithm adaptively acquiring correction parameters is proposed. According to degree of exposure, the variation range of parameter γ is selected the step size is set. Gamma transformation is then implemented sequentially for the luminance component. Furthermore, the root means square error of enhanced image and source image, as well as the information entropy of enhanced image are sequentially obtained. The best balance point is captured, yielding corresponding γ for image recovery. Finally, the image is segmented based on histogram equalization principle to achieve the best recovery performance. The experimental results show that the algorithm is able to obtain the correction parameters adaptively and recover true color of color bar code images effectively under low-light or non-uniformly intense light of particular light sources.
Research on Data Mining Method of TCM Prescription Based on Machine Learning
Authors: Wen Gege , Wen Pin , Tang Zukai 
Published: June 2021

Abstract
This paper uses conditional random field model to mine TCM data. In order to identify the information of prescriptions in ancient Chinese medicine books, the model firstly cleaned and segmented the original text data, then, labels are added according to the selection of TCM characteristics to mark part of the corpus, and according to the demand divided into two parts, the training corpus was used to adjust the parameters of the custom CRF feature template, and the model was generated after adjusting the parameters. The accuracy of the model for entity recognition was verified by using the test corpus. In the end, the feasibility of the conditional random field model in the identification of TCM prescriptions was verified experimentally.
Dual bidirectional mutual distillation based on dense connections
Authors: Lu Bin 
Published: June 2021

Abstract
In the field of deep neural network, model compression has been widely used. As a model compression method, knowledge distillation has been studied more and more widely. Most of the existing knowledge distillation methods need a lot of data to train the teacher model in advance, and the interaction between their teacher model and student model is often weak. The existing methods such as DML are constrained by loss to realize soft weight sharing, and there is little work to discuss hard weight sharing. Therefore, a new deep interactive online distillation method is proposed in this paper. In this method, the teacher network generates the auxiliary student network and establishes the connection between the two, and the student network generates the auxiliary teacher network and establishes the connection between the two. Then the two auxiliary networks are combined and trained from scratch. Finally, the performance of the model is improved by using back gradient propagation. The effectiveness of this method is verified by experimental tests on common image classification tasks.
Computer Image Processing System Based on Feature Extraction Technology
Authors: Luo Xiaojing , Cai Guangxing 
Published: June 2021

Abstract
This article studies the methods of computer image acquisition, processing and recognition. The study found that the images collected from the computer image processing system often have blurring phenomenon. For this reason, the paper proposes a wavelet transform feature extraction algorithm based on colour images, which can separate the image and the background without greying the colour image. Through experimental research, it is found that this method can efficiently and quickly solve the problem of automatic image registration.
Rapid Flood Mapping based on Remote Sensing Cloud Computing and Sentinel-1
Authors: Li Jianfeng , Wang Jiawei , Ye Huping 
Published: June 2021

Abstract
Flood is a natural disaster with the highest frequency and the widest influence range in the world, which seriously restricts human survival and economic development. Obtaining flood inundation range quickly and accurately has important practical significance for flood disaster assessment, rescue and resource allocation. Based on Google Earth Engine cloud platform and Sentinel-1 radar images, a quick flood inundation range extraction model is constructed and applied to the analysis of severe floods in Matala District, Sri Lanka in May 2017. The results show that the flood in Matala area is mainly concentrated in the south and southwest, and the surface water area has expanded by 5 times, among which the flood area in Thihagoda district is the most serious, accounting for 29.28% of the total area. The model has high efficiency and strong feasibility, which can be widely used in flood emergency monitoring.
High Spatial Resolution Remote Sensing Image Classification Based on Pixel Shape Index Method
Authors: Kong Hui , Liu Jin Bao 
Published: June 2021

Abstract
Then the pixel shape index method is used to extract the pixel shape index features of high spatial resolution remote sensing images and supplement the spectral features.The experimental results show that the pixel shape index feature can effectively distinguish the ground objects with similar spectral features but different geometric shapes, and is superior to the spectral feature classification method in accuracy.Compared with the small ripple feature method and the multi-scale region feature method, the pixel shape index method also achieves better results.On the other hand, it is found in the experiment that the method is easily affected by the detailed information in the high spatial resolution remote sensing image, and the classification effect of the region rich in detailed information is not ideal.In this paper, pixel shape index method is used to effectively distinguish ground object targets with similar spectral features but different shapes, and extract pixel shape index features of high-resolution remote sensing images. Compared with spectral features, pixel shape index features have great advantages in accuracy, and it is also conducive to the supplement of spectral features.
Research on the Machine Learning Algorithms in the Prevention Model of New Coronary Pneumonia Epidemic Prevention and Control
Authors: Hong Yukai 
Published: June 2021

Abstract
In response to the practical application of new COVID-19 transmission dynamics models such as new coronary pneumonia, the number of contacts associated with real-time data of confirmed cases is proposed, and the new COVID-19 transmission dynamics model based on machine learning algorithms is optimized. Enhance the practical value of the model. This type of exposure can be extended to other new cases of COVID-19, revealing the risk of transmission at different times during the outbreak.
Research on the Application of Rigid-flexible Compound Driven Fruit Picking Robot Design in Realizing Fruit Picking
Authors: Yang Zhuoyuan 
Published: June 2021

Abstract
This article mainly designs an efficient fruit picking robot that combines rigidity and flexibility. The design of the robot adopts a mechanical arm with 5 degrees of freedom for the mecanum wheel. The mechanical arm is equipped with a gripper made using artificial muscle technology. The ZED2 binocular vision system is used to obtain spatial coordinates to realize the positioning and positioning of the mechanical arm. Video image acquisition. In order to realize the automatic recognition and picking of strawberries, this paper creates a strawberry training set, and uses the yolov3 algorithm to train the data and obtains a strawberry recognition model, which has a recall rate of larger strawberries and immature strawberries and the precision rate reached 90%, and the detection precision and precision rate of small target strawberries reached more than 80%. It can Meet the requirements of picking in real life.
Large parallax image matching based on Graph Matching
Authors: Sun Guangmin , Weng Yu , Shi Chong 
Published: June 2021

Abstract
In the security video surveillance system, image splicing technology faces the difficulty of achieving large parallax and small overlap area. This paper summarizes the traditional stitching technology and analyzes that the image matching algorithm is the key technology to solve the problem of large parallax stitching. Aiming at the problems that traditional matching methods with low matching accuracy cannot adapt to large parallax non-rigid transformatio, a model based on image matching is proposed in this paper for splicing tasks. The model which combines the similarity of the global structure and the local structure can not only divide the feature point on the structural level but also can pair with each other. At the same time, the framework can be trained end-to-end. The training samples composed of simulation data sets are adopted to train the designed model which is tested and verifyed by the test sample sets composed of three kinds of mixed data. Compared with PCA-GM, the model proposed in this paper adapts well to splicing tasks. The matching accuracy can achieve 93.1% and 91.9% on the simulation data set and the small parallax splicing data respectively, which can initially solve the image matching problem in the traditional image splicing scene. However, a further research aiming at the problem of the matching accuracy on the large parallax data set can only achieve 74.5% will be needed.
Research on the Effect of Online Service by Logistic Regression Model and Remote Video Technology
Authors: Zhang Hanping 
Published: June 2021

Abstract
At present, online teaching is developing rapidly, and with it is how to improve the effectiveness of online teaching. This research constructs learning behavior indicators from the online learning process, and uses logistic regression to analyze the learning data in online teaching, and explores the impact of learners’ online learning behavior on academic performance. The correlation between indicators such as the time lag of course registration, the number of login courses, the number of submitted homework tests, the average of the number of saved exercises, and the degree of completion of video viewing and other indicators and scores were discussed. The study found that the submission of homework tests can be used as key indicators for online teaching and learning performance prediction.
Control design based on multi-index nonlinear control method
Authors: Zhao Zhaolong , Huang Minghui , Li Yibao 
Published: June 2021

Abstract
Firstly, a nonlinear control system based on the Riemannian manifold is defined from the perspective of holism, and the expression of the system’s state equation in the local coordinate system of the Riemannian manifold is given. The geometric structure of the Riemannian manifold is discussed. The influence of nonlinear systems is studied. The controllability and observability of nonlinear systems is studied. Secondly, using the properties of involute distribution and fully geodesic submanifolds, the local parts of nonlinear systems based on Riemannian manifolds are given. Controllable structured decomposition, local observable structure decomposition and Kalman decomposition. Thirdly, using the properties of orthogonal involute distribution family, incremental involute distribution family and fully geodesic submanifold family, the research is based on Riemannian manifolds. The parallel decoupling problem and cascade decoupling problem of nonlinear control systems on the above, as well as the local disturbance decoupling problem of affine nonlinear control systems.
Experimental Research on Reasonable Time Difference of Electronic Detonators Based on MATLAB
Authors: Wang Jing 
Published: June 2021

Abstract
At present, there is relatively little research on the selection of reasonable time difference of digital detonators. When choosing delay time in engineering, it mainly depends on engineering experience and empirical formula, which lacks reasonable scientific basis. In this paper, based on a large number of experimental vibration monitoring data, through numerical simulation, the influence of blasting time difference on peak velocity, main vibration frequency and instantaneous energy of blasting vibration waveform is analyzed in detail, and the best time difference is determined comprehensively.
Research on automatic generation method of trusted test scenarios based on behavior declaration
Authors: Yu Xuejun , Wang Liping 
Published: June 2021

Abstract
In today’s Internet era, the development of application software gradually becomes mature, which makes people’s working mode also change accordingly. Applications, however, the scale is expanding constantly, make the internal structure of software is more complex than before, the application of the environment has become more open, causing the site to be attacked events occurred frequently, makes the credibility problem of software, especially in security, reliability, availability, and so on several aspects. At present, the research on software credibility is mainly based on application demand and software behavior, which are closely related to software credibility. Under this architecture. However, too many researchers focus on whether the application system meets the application requirements, and do not focus on the credibility before the system design. Therefore, this paper mainly takes the behavior statement as the entry point to study whether there are some untrustworthy factors before the system design.
Optimized control design of sliding plug door for high-speed EMU
Authors: Yang Dapeng , Li Yang , Liu Changying 
Published: June 2021

Abstract
With the continuous improvement of people’s requirements for the convenience of going out, high-speed EMUs came into being. As one of the important components of the high-speed EMU, the sliding plug door is located at the junction between the carriage and the carriage, and is one of the important passages for passengers. When the EMU is running at high speed, once the control system fails, it will cause damage to the personal safety and interests of passengers. Therefore, as the speed of the EMU increases, the stability of the electric end door control system of the EMU also needs to be enhanced. First, analyse the motor drive process of the sliding plug door control system, design the sliding plug door control system at the hardware level, and build an experimental platform for analysis and testing. Because the operating environment of high-speed EMUs is very complicated, in order to make the sliding plug door control system work stably for a long time, it is necessary to analyse the overall process of the sliding plug door opening and closing movement in stages, and adopt fuzzy-PID combined control. The algorithm further optimizes the movement process. The control method of the brushless DC motor is digital control, and the rotation speed of the motor is controlled by controlling the duty ratio applied to the motor side. Finally, through experimental testing, it is verified how the fuzzy-PID combined control algorithm can improve the stability of the door opening and closing movement.
Design of analog Filter circuit based on Multisim
Authors: Fu Siyong 
Published: June 2021

Abstract
With the rapid development of electronic technology, the filter circuit has been widely used in various occasions. Therefore, based on the study of various analog filter circuit principles, this paper users the Multisim simulation platform to design the corresponding low-pass and band-pass filters. In order to verify the performance of the filter circuit, the normal signal, noise signal and filter output signal are respectively observed by oscilloscope after noise is introduced, and then the performance is analyzed according to the waveform and parameters. The experimental results show that all the designed filters can achieve good filtering effect.