bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application Analysis of Face Recognition Technology in Video Investigation
Authors: Qiu Bo 
Published: November 2020

Abstract
This article analyzes the basic application process of face recognition technology. The content of this article includes face information collection, face information detection, face information recognition, synchronization warning function, etc. The author optimized the basic conditions by studying and optimizing the system operation algorithm, and at the same time strengthened professional training and information security management. In normal times, we apply face recognition technology in the detection and solving of suspects in the identification of suspects, and in the detection of crowded scenes and multiple scenes. In addition, face recognition technology will also be applied to road safety precautions and residential area safety precautions. The purpose of this article is to improve the application effect of face recognition technology in work, and to provide more convenient conditions for the smooth development of investigation work.
A Research on Online Grammar Checker System Based on Neural Network Model
Authors: Hao Senyue , Hao Gang 
Published: November 2020

AbstractGrammar error correction is one of the most important research fields in natural language processing, while grammar checker is the tool to help people correct grammatical error. We focus our work on researching a better approach to an online grammar checker system to help people correct the grammatical error in their text more precisely, user-friendly, and efficiently. This new method of online grammar checker system is based on neural network model, Transformer, and is able to detect about 25 different types of grammatical errors in the text. We did the evaluation on our new online grammar checker system, and the final experiment results proved that we have found a good approach to the online grammar checker system.
Fire recognition with convolutional neural network based on transfer learning
Authors: Liu Xiang , Wei Guangcun , Rong Wansheng , Xiao Xinguang 
Published: November 2020

Abstract
The fire recognition model based on deep learning can avoid many defects in the traditional method, but its construction requires a large amount of data to train the network parameters, and it takes a lot of time. In order to improve the accuracy of the model, this paper proposes a fire recognition model TNVGG-19 (Transfer learning + Newly fully connected layer module + VGG-19) with convolutional neural network based on transfer learning. First, we use the strategy of transfer learning to train the feature extraction network. Secondly, based on the VGG-19 model, this paper adds a newly designed fully connected layer module. Considering that flame data belongs to small sample data, we adopted a data augmentation strategy. Experiments show that the TNVGG-19 fire recognition model based on transfer learning proposed in this paper can effectively improve the accuracy of fire prediction and reduce the false alarm rate.
Cancer image classification based on DenseNet model
Authors: Zhong Ziliang , Zheng Muhang , Mai Huafeng , Zhao Jianan , Liu Xinyi 
Published: November 2020

Abstract
Computer-aided diagnosis establishes methods for robust assessment of medical image-based examination. Image processing introduced a promising strategy to facilitate disease classification and detection while diminishing unnecessary expenses. In this paper, we propose a novel metastatic cancer image classification model based on DenseNet Block, which can effectively identify metastatic cancer in small image patches taken from larger digital pathology scans. We evaluate the proposed approach to the slightly modified version of the PatchCamelyon (PCam) benchmark dataset. The dataset is the slightly modified version of the PatchCamelyon (PCam) benchmark dataset provided by Kaggle competition, which packs the clinically-relevant task of metastasis detection into a straight-forward binary image classification task. The experiments indicated that our model outperformed other classical methods like Resnet34, Vgg19. Moreover, we also conducted data augmentation experiment and study the relationship between Batches processed and loss value during the training and validation process.
AGA-GRU: An Optimized GRU Neural Network Model Based on Adaptive Genetic Algorithm
Authors: Bai Chenyao 
Published: November 2020

Abstract
The weight adjustment of gated recurrent unit (GRU) network depends on the gradient descent algorithm heavily, therefore this paper proposes an improved GRU neural network model based on adaptive genetic algorithm (AGA-GRU) to solve this problem. In this model, AGA is used to construct the optimization system, and the parameters of neural network model are optimized to improve the prediction performance. The results on UCI dataset show that the prediction accuracy of AGA-GRU model is significantly improved, and the generalization performance is stronger.
A Multi-Criteria Band Selection Method of Hyperspectral Images
Authors: Wang Zhonghua , Zhao Yulin , He Bangsheng , Wang Hao 
Published: November 2020

Abstract
Hyperspectral remote sensing images are characterized by many bands and large amounts of datum, which require the dimensionality reduction, but band selection is one of the basic methods for dimensionality reduction of hyperspectral datum. For this reason, this paper proposes a multi-criteria band selection method. Firstly, the intrinsic dimensionality of hyperspectral image is calculated by virtual dimension, then the subspace is divided according to the band correlation criterion; and secondly, the information criterion is adopted to select the high-quality exponential bands in each subspace; and finally, the most suitable bands in each subspace are selected by class separability criterion to form the optimal band subset. Various experiments show that, compared with the optimum index factor and adaptive band selection methods, the proposed method has better performance in the measurement of information volume and information redundancy.
Terminal Credibility Analysis Method for “One End Multi Network” Environment
Authors: Bin Dongmei , Yang Chunyan , Ling Ying , Yu Tong , Li Xin 
Published: November 2020

Abstract
it is necessary to monitor the terminal’s credibility continuously in both the government external network and the government internal network. When the terminal’s credibility is damaged and does not meet the network requirements, the terminal’s network access behavior will be blocked. To monitor the terminal’s credibility continuously, it is necessary to collect and send the terminal’s hardware assets, software assets, security configuration, security loopholes, network behavior, user behavior and other information to the management platform, which conducts machine learning, association analysis, statistics and so on, so as to give the terminal’s credibility in the current state. To analyze the credibility, the terminal needs to continuously monitor the change of the collected information and send it to the management platform without omission. However, in the “one end multi network” environment, the terminal access network is constantly switching, each network has its own management platform, each management platform can not reach each other’s network. Therefore, for the terminal, due to the switching of the network, its credibility analysis data is incomplete in a single network, which will affect the accuracy of the platform for terminal credibility analysis. The invention provides a method for enabling the management platform to obtain complete credibility analysis data.
APSO-LSTM: An Improved LSTM Neural Network Model Based on APSO Algorithm
Authors: Chen Keqiao 
Published: November 2020

Abstract
In the LSTM neural network model, the updating of the weights and threshold parameters depends on the gradient descent algorithm. When the number of hidden layers increases, the convergence rate decreases, and the adjustment of the weights may fall into local extremum, which affects the generalization ability and prediction performance of the LSTM model. Based on this, this paper proposes an improved LSTM neural network model based on APSO algorithm (APSO-LSTM). In this model, the root mean square error is designed as the fitness function, and APSO algorithm is used to build the optimization system. In the verification stage, the weight parameters of each neuron are globally optimized to improve the prediction performance of the model. The experimental results on time series datasets of UCI show that the prediction performance of APSO-LSTM model is significantly better than that of the standard LSTM model, which verifies the rationality of the model.
An Empirical Study on the Reform of Teaching Mode in Western Universities -- Taking the online and offline blended teaching reform of “Computer Foundation” in Xinjiang University as an example
Authors: Liao Yuanyuan , Liu Yan 
Published: November 2020

Abstract
The course “computer foundation” bears the responsibility of cultivating the future national information literacy. Facing the fact that the overall computer ability of freshmen in western universities is low and the teaching effect is not optimistic, the teaching reform is urgent. This paper takes the online and offline blended teaching reform of “computer foundation” in Xinjiang University as an example. Through the exploration and practice of the mixed teaching mode of “multivariate-six order”, the overall performance of students shows an upward trend, and students have a high degree of recognition and acceptance of the blended teaching. Facts have proved that blended online and offline teaching is effective in the practice of this course.
Interference classification and identification of TDCS based on improved convolutional neural network
Authors: Li Ming , Ren Qinghua , Wu Jialong 
Published: November 2020

Abstract
A classification and recognition algorithm based on short-time Fourier transform and convolutional neural network (STFT-CNN) is proposed to solve the common interference signal classification and recognition problem in transform domain communication systems. In this algorithm, the time-spectrum diagram of interference signals obtained by short-time Fourier transform is input into the vggnet-16 network model improved according to STFT characteristics for feature learning and training, and the classification and recognition of signals are completed. Simulation results show that the proposed algorithm for comprehensive recognition rate reached 97.7%, 6 kinds of jamming signal in low SNR circumstance still can reach more than 93% recognition rate, compared with the traditional algorithm, this method not only improves the classification recognition rate of single interference, but also improves the recognition of mixed interference ability, has the ability to resist low signal-to-noise ratio, makes the transform domain communication system can choose transform domain for anti-interference, provides theoretical basis and support for the application of convolutional neural network in anti-interference of communication system in transform domain.
A Review on Neural Style Transfer
Authors: Li Jiayue , Wang Qing , Chen Hong , An Jiahui , Li Shiji 
Published: November 2020

Abstract
Image style transfer is a method that can output styled images, which can both retain the original image content and add new artistic style. When using neural network, this method is referred as Neural Style Transfer (NST), which is a hot topic in the field of image processing and video processing. This article will provide a comprehensive overview of the current NST methods. Firstly, we introduce the current progress of NST from two aspects: the image-optimisation-based method and model-optimisation-based method. Then we compare and summarize different types of the NST algorithms. The review concludes with a discussion of applications of NST and some proposals for future research.
Ultrasound Image Segmentation of Thyroid Nodules Based On Joint Up-sampling
Authors: Wu Junxia , Zhang Zhenqing , Zhao Juanjuan , Qiang Yan 
Published: November 2020

Abstract
In view of the problem of inaccurate segmentation of thyroid nodule ultrasound images, a method of thyroid nodule ultrasound image segmentation based on joint up-sampling is proposed. In order to solve the problem of serious speckle noise in the original ultrasound data, preprocessing is performed to enhance the contrast between the nodule area and the background area. In order to achieve accurate positioning of the nodule target area, a joint up-sampling module is designed to fuse the context information of ordinary standard convolution and expansion convolution with different expansion coefficients. Experimental results show that the proposed method achieves 93.19% accuracy and dice similarity coefficient is 0.8558, which is better than other existing thyroid nodule segmentation network models.
Improving Convolutional Neural Network Expression via Difference Exponentially Linear Units
Authors: Hu Zhiying , Huang Hongbo , Ran Qinglin , Yuan Mingyang 
Published: November 2020

Abstract
Convolutional Neural Network (CNN) has been applied to various tasks with great success. Adding an activation function is an important way to introduce nonlinearity into convolutional neural networks. The commonly used activation functions mostly use different forms of negative feedback for the negative input, however, some researchers have recently proposed positive feedback methods for the negative input such as Concatenated Rectified Linear Units (CReLU) and Linearly Scaled Hyperbolic Tangent (LiSHT) and achieved better performance. To explore this idea further more, we propose a new activation function called Difference Exponentially Linear Unit (DELU). The proposed DELU activation function can optionally provide positive and negative feedback for different values of negative inputs. Our experimental results on the commonly used datasets such as Fashion Mnist, CIFAR10, and Imagenet show that DELU outperforms other six activation functions, Leaky ReLU, ReLU, ELU, SELU, Swish, SERLU.
Research on HRRP target recognition based on one-dimensional stack convolutional autoencoder
Authors: Zhang Guoling , Wang Xiaodan , Li Rui , Lai Jie , Xiang Qian 
Published: November 2020

Abstract
Extracting key features from the radar high resolution range profile (HRRP) determines the accuracy and reliability of radar target recognition. Aiming at the problem of feature extraction and recognition in HRRP target recognition, a one-dimensional stack convolutional autoencoder (1D-sCAE) recognition method is proposed. Firstly, one-dimensional convolutional autoencoder is constructed to extract the deep features of one-dimensional HRRP signals by unsupervised learning. Then, multiple one-dimensional convolutional autoencoders are stacked to construct 1D-sCAE, and HRRP is classified by fine-tuning the network with label data. At the same time, for the overfitting problem of 1D-sCAE, dropout technology is added to optimize and improve the generalization performance. Through the simulated HRRP data of the target in the middle part of the trajectory, the experiment shows that the proposed algorithm has better feature extraction capabilities, higher recognition accuracy and stronger robustness than the typical deep neural networks.
Accurate Monocular Depth Estimation via Interaction of Hierarchical Features
Authors: Zhang Jiahao , Chen Dan , Lin Ye , Wu You 
Published: November 2020

Abstract
Monocular depth estimation is a challenging task, which assists in understanding 3D scene geometry from the same 2D scene. The ordinal-regression-method demonstrates superior performance in this issue but naive ordinal inference strategy for inferring the final depth values and naive operations to up-sample to the desired space scale introduce significant discretization errors and object boundary confusion. Firstly, we come up with a novel inference strategy to reduce the discretization errors. And then a specifically designed decoder that completes the fusion of different hierarchical features under guidance and the fusion feature reconstruction. We evaluate on a public monocular depth-estimation benchmark dataset (NYU Depth V2). The experimental results show that the method proposed outperforms other ordinal regression methods.
Method for extracting crack area on structure surface based on digital image processing technology
Authors: Jiang Song , Zhang Hua 
Published: November 2020

Abstract
According to the characteristics of the surface crack of the wall structure, the method of extracting and marking the surface crack area of the wall mechanism is studied by using the digital image processing technology. In this paper, the wall structure crack is taken as the research object, firstly, the nonlinear Bilateral Filters algorithm is used to suppress the noise and smooth the image; then the crack edge is extracted by Canny operator, all the crack edges are closed by mathematical morphology opening operation, and some non-crack contours are eliminated; finally, the non-crack area is deleted to realize the real mark of crack connected region. The analysis of the experimental results shows that the method proposed in this paper can realize the safe line monitoring of the cracks in the wall structure and mark the area where the real cracks are located.
Image Classification of Chicken Embryo Based on Matched Filter and Skeleton Curvature Feature
Authors: Jin Xue-Song , Li Jing , Du Xin 
Published: November 2020

Abstract
To solve the problem of nondestructive detection of egg formation activity in vaccine preparation, a new method based on matching filter for chicken embryo image blood vessels segmentation algorithm combined with the curvature feature of the blood vessels skeleton was proposed. Gaussian matching filter was used to enhance the contrast between the blood vessels and the background of the green channel image and the blood vessels were segmented from the background. Morphological methods was used to process the binary images to extract the vascular skeleton feature. The curvature of the vascular skeleton was calculated by using the least square method. Finally, the activity of chicken embryo was judged by combining the shape feature of blood vessel with the texture feature of chicken embryo extracted by gray level co-occurrence matrix algorithm. The experimental results show that this method has high accuracy in the classification experiment of chicken embryo images affected by spot noise, and meets the quality requirements of producing virus species in vaccine preparation.
Slave Coordinated Speed Analysis and Coordinated Control Method
Authors: Yan Hang , Sun Xuan , Ai Changsheng , Ren Gangchang , Zhang Baixing 
Published: November 2020

Abstract
When the container is being repaired, it is necessary to transfer the container to be repaired between the temporary storage area and the repair area. Due to the large size, when manually transported by forklift, the line of sight is blocked, there is a great safety hazard and efficiency. It is relatively low, and it is inconvenient to use a single mobile robot to carry it. Therefore, to solve this problem, a dual-mobile robot collaborative operation system is developed, and two mobile robots are used to move containers together to complete container maintenance and handling. Through the analysis of the cooperative handling of the dual mobile robots, a mathematical model of the cooperative movement of the dual mobile robots is established, and kinematics analysis is carried out to provide a theoretical basis for controlling the cooperative operation of the dual mobile robots.
A surface defect detection system for railway track based on machine vision
Authors: Zhao Kang , Luo Laizhen , Ren Zhengmin , Fu Qingchen 
Published: November 2020

Abstract
In order to detect rail surface defects more quickly and effectively, a rail surface defect detection system based on machine vision was constructed. A perfect lighting system is built to obtain high quality image information. At the same time, the linear CCD camera is used to collect the rail information, which is uploaded by FPGA, and the image information is processed and analyzed by MATLAB software in the upper computer. Through the methods of image denoising and image enhancement, the image quality is improved, and the image information suitable for operation is obtained; then, the rail positioning is completed by threshold segmentation, gray compensation and image binarization to simplify the image operation; finally, the final positioning of defects is realized through edge detection and feature extraction. By building a test platform on the rail maintenance vehicle, the rail surface defect information collected by the rail maintenance vehicle is analyzed and processed.
A novel scissor-type anti-shock seat employing rotary magnetorheological damper
Authors: Wang Tao , Dong Xiaomin , Yu Jianqiang , Li Xin , Yan Maosen 
Published: November 2020

Abstract
In order to improve the cushioning performance of anti-shock seat in an impact environment and protect the lives of occupant to the greatest extent, the cushioning performance of a new type of anti-shock seat combining a rotary magnetorheological (MR) damper and a scissor seat was studied. Aiming at the “bottoming out” characteristic of scissor seats after the cushioning stroke is exhausted, and the dynamic model of scissor seats with MR damper is established. In order to test the cushioning performance of the new anti-shock seat in an impact environment, a prototype MR damper was processed and assembled, and the cushioning performance of the seat under different impact speeds was tested using a drop impact test platform. The test results show that the new anti-shock seat can adjust the cushion stroke and cushion force value to keep the acceleration limit of the occupant within the tolerance range, thereby effectively reducing the risk of injury.
A hardware-in-the-loop simulation platform for distributed UAV swarms
Authors: Wang Zhenbei , Li Jie , Yang Chengwei , Yang Yu 
Published: November 2020

Abstract
To improve the functional verification efficiency of intelligent UAV swarm systems, this paper proposes a distributed UAV swarm hardware-in-loop (HIL) simulation platform. The proposed platform uses fine-grained 3D scenes and real aircraft aerodynamic models as inputs to simulate the control and mission decision-making process of the UAV swarm by building a swarm data interaction network. The simulation platform is equipped with real on-board electrical equipments, which can fully verify the compatibility between software and hardware in the UAV swarm system and is able to shorten the research cycle effectively. The platform supports the addition and deletion of all-digital virtual simulation nodes and can be used for large-scale swarms. The tests on individual strap-down terminal guidance and multi-aircraft coordinated search show that the platform has good simulation results for features such as data interaction, autonomous decision-making, and control of UAV swarms.
Research on Aerial Robot Based on Visual Servo
Authors: Deng Tianchen 
Published: November 2020

Abstract
Towards target grasping by an aerial manipulator, an aerial manipulator system is presented, for which a separated control strategy is adopted. In this paper, a suitable adaptive visual servo controller is proposed based on the original aerial manipulator system, and a visual servo controller is designed based on the depth independent image Jacobian matrix. Thus, the grasping motion of the manipulator is controlled by the visual servo of the monocular camera, which can greatly enhance the functionality and speed of the aerial manipulator system. Furthermore, a unique single degree of freedom joint collocation camera is designed to enable the camera to lock the target object at any time during the motion of aircraft. Finally, this paper designs a unique state machine system, which can make the control system switch into any state.