bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Image Super-resolution Internet Transmission Technology Based on Virtual Optics
Authors: Xiao Gengyi 
Published: June 2021

Abstract
Image resolution is a set of performance parameters used to evaluate the richness of detailed information contained in an image, including temporal resolution, spatial resolution and color scale resolution, etc., which reflects the ability of the imaging system to reflect the detailed information of objects. In this paper, a super-resolution image transmission technology based on virtual optics is proposed. The original image is placed on the object plane of the virtual light path. Firstly, the light wave signal of the original image on the virtual diffraction plane is obtained by inverse diffraction calculation. Then, the light wave of the virtual diffraction plane is illuminated by spherical wave and then the forward diffraction calculation is performed. By changing the position of the observation plane, the original images with different magnification can be reconstructed. The simulation test results show that compared with the general interpolation amplification method, the amplified image has a good visual perception effect, especially in the significant area. The results obtained by the algorithm in this paper are better than the interpolation model and convolutional neural network algorithm as a whole; The above test results objectively show that the super-resolution effect of this algorithm is better.
Research on the Aesthetic Features and Technology of Computer Graphics
Authors: Song Yanbin 
Published: June 2021

Abstract
Computer graphics is a new science produced with the popularization of computers. The processing technology of graphics and images in computer graphics has attracted much attention. How to make reasonable use of them has become an important subject in the field of modern scientific applications. For this research background, this paper proposes an image recognition algorithm based on wavelet and DFB-PCA. After decomposing the recognition image with wavelet transform, DFB-PCA recognition is performed on the low frequency subband. The experimental results show that the method is beneficial to reduce the influence of factors such as illumination, posture and expression, reduce the dimensionality of noise and image recognition, and has better robustness. Compared with directly using PCA recognition on the original image, the calculation speed and recognition efficiency of the image can be improved.
Inspection Robot for Submarine Pipeline Based on Machine Vision
Authors: Yin Fangrui 
Published: June 2021

Abstract
In order to improve the intelligence of submarine pipeline inspection, reduce manpower and material resources, and improve inspection efficiency, an underwater pipeline inspection robot based on machine vision is proposed. This paper introduces the existing pipeline inspection methods and technologies, and analyzes the feasibility of pipeline inspection and inspection technologies based on image processing. On the basis of this subject, the corresponding circuit and embedded system are designed, and the related images are sampled, processed and analyzed, and the images are subjected to threshold segmentation, binarization, edge corrosion and other operations. Furthermore, the experimental site and underwater robot were built, and the pipeline defects were detected in real time by using the recognition method based on neural network.
Research on Detection Algorithm of Roadway Line on Structured Road Based on Vision
Authors: Qian Suke , Yang Jie , He Chao , Li Zonghao 
Published: June 2021

Abstract
To solve the problem of low efficiency of lane recognition in different structured road environments, an improved Hough transform algorithm is proposed. Firstly, every frame of image in the video stream is processed by gray scale and inverse perspective transformation, so as to reduce the amount of data and improve the accuracy of lane recognition. Then, median filtering and wavelet hard threshold denoising are combined to remove noise and improve image quality. Secondly, the image is binarized and edge detected by OTSU algorithm and Sobel operator, and the lane feature information is extracted. Finally, the traditional Hough transform algorithm is improved, and the slopes and points of the selected straight lines in the feature area are averaged, and then a new straight line is obtained and displayed in the image. Simulation experiments show that the algorithm has good applicability and reliability in different structured road environments, and achieves the expected experimental results.
Study on High Precision Fabric Density Measuring Instrument and Measuring Method Based on Moire Fringe
Authors: Wen Xinhui , Sun Haoteng , Wang Meiyan 
Published: June 2021

Abstract
With the continuous development of science and the continuous progress and reform of fabric knitting technology, all kinds of fabrics continue to appear. Linear density is one of the most important physical and geometric characteristics of knitted fabrics, which affects the physical and mechanical properties, feel and style of textiles, and is one of the important bases for fabric design and manufacturing process parameters.In this paper, a device and method is developed, which is not affected by the width and density of fabric lines, and can accurately measure the density of fabric. The invention puts forward a method of measuring fabric density from a new point of view. Using the phenomenon of moire fringes, the fabric density of the fabric to be measured is calculated by measuring the angle. The device has the advantages of simple structure, low production cost, and is not affected by the width and density of cloth lines. it can measure fabrics with high line density or wide lines, and has the advantages of high precision and convenient operation. and the gratings with different grating constants can be selected according to the characteristics of the fabric, which has a very wide range of application.
Application of Fuzzy Recognition in Image Information Recognition
Authors: Huang Junlian , Zhao Dongyan , Lv Boxue 
Published: June 2021

Abstract
Image recognition is to calculate and analyze the information contained in the image, classify it or extract useful information from it. Image recognition technology based on fuzzy recognition is a new type of image recognition technology developed with the development of modern computer technology, image processing, artificial intelligence, pattern recognition theory and so on. It is an image recognition method based on traditional image recognition methods and fusing fuzzy recognition algorithm. This paper verifies the fuzzy recognition program, reads the pixel matrix of the file, and gets a relatively clear image after certain program processing. The simulation results show that the fuzzy recognition method can get more clear and accurate information in the application process of image information technology.
Perceptual losses for self-supervised depth estimation
Authors: Liu Xiaoyu , Gao Huachen , Ma Xiaowen 
Published: June 2021

Abstract
Convolution neural network has shown excellent results in stereo and monocular disparity estimation, while most of the existing methods convert the image depth prediction problem into the image reconstruction problem, and calculate the depth of each pixel through the disparity between the generated left and right images. However, in the reconstruction task, the loss is still calculated at the pixel level when comparing the reconstructed picture with the original picture, which will greatly affect the estimation of picture depth due to the problems of illumination and occlusion. Therefore, when calculating the loss of image reconstruction, it is very important to compare the higher-level features extracted from the reconstructed image with the original image. In this paper, based on the existing methods, we have innovated the loss function and introduced perceptual loss, i.e., we use feedforward neural network to extract features to further evaluate the reconstructed image, to make the reconstruction loss of baseline more accurate and improve the accuracy and robustness of the depth prediction model. To compare the improved effect, we performed extensive experiments on KITTI driving data by the improved model set, and the experimental index obtains better performance than the original baseline model.
Asian Giant Hornets Recognition Using Deep Convolutional Neural Network
Authors: Wang Haochen , Yang Ruofeng , Li Xu 
Published: June 2021

Abstract
We trained 2 deep neural networks to identify whether the images with high resolution in the dataset provided by COMAP contain any Asian giant hornet. We divide the classification problem into two subproblems: feature extraction, and image classification. In order to reduce the impact of sample imbalance, we apply image flipping and Borderline-SMOTE methods for data augmentation first, and then divide the data into the training set and the validation set (testing set). Next, we utilize auto-encoder to collect key features of images and the testing loss dips to 0.0274 after training. Next, we establish the Classification-Net to solve the identification problem based on the features just extracted, and the accuracy in testing set reaches 0.8030. Finally, we summarize the main features of having negative labels from three aspects: species characteristics, subject definition and background softness.
Pavement Crack Detection Method of Street View Images Based on Deep Learning
Authors: Shu Zekai , Yan Zhaoyu , Xu Xihang 
Published: June 2021

Abstract
Pavement crack detection is a challenging task for carrying out pavement maintenance works. Deep learning method is regarded as an effective and accurate way to detect pavement cracks. However, this often requires a large dataset composed of different crack images. This paper introduces a convenient and low-cost method to collect pavement images by using street view images. 400 images from 5 cities are collected and labeled to form the dataset. Then, it is applied to train a target detection network YOLOv5, which is the latest version of YOLO network. The result shows that this network can effectively detect crack with mAP of over 70% and detection time of 152ms, which are all better than another classical method YOLOv3. Considering the easiness of collecting images, this method can be a suitable way to evaluate the pavements.
Research on Machine Vision Effect Based on Graph Neural Network Decision
Authors: Huo Jide , Sun Weining , Dai Huaixuan 
Published: June 2021

Abstract
With the continuous expansion of the scale of the network, modeling the complex graph structure is a major challenge for the recommendation task. There are conflicts between these complex information, which will directly affect the recommendation results. For this reason, a graph neural network recommendation method based on multi-branch decision is proposed. The algorithm models the social network graph and the user project graph through the neural network, connects the two graphs intrinsically, and learns the feature vectors of the target users in the social space and the project space. Then two feature vectors are connected in series by MLP to extract the potential feature vector of the user. Finally, the prediction score is generated by integrating the probability matrix decomposition model. In this method, the graph neural network is used to model the program data and dependence graph, and the effective program features are automatically extracted from the source code, and then the extracted features are input into the downstream model for loop vectorization parameter prediction. Finally, the attention mechanism is used for information fusion to get the final session representation and predict the next interactive item. Comparative experiments are carried out under the two scenarios of e-commerce and civil aviation respectively. The experimental results show that, compared with the optimal benchmark model, the improvement of MGSP model on each index of e-commerce data set is more than 1%, and that of civil aviation data set is about 3%, which verifies the effectiveness of MGSP model. On the LLVM cyclic vector test set, the proposed method achieves a speedup of 2.08 times and improves the performance by 12% compared with the existing methods. In order to solve the problem that the existing deep learning model considers the program code as a serial sequence and misses a large performance optimization space, a new program heuristic method based on depth map network is proposed to achieve optimization.
Research and Application of LDA Model in Movies Images Based on the Visual Effects of Computer Images
Authors: Zheng Yitian 
Published: June 2021

Abstract
With the advent of the image age, the popularity of war-themed film and television dramas in the new century has promoted a new wave of thematic narratives. Facing the era of computer graphics, the main theme narrative adopts a series of new reconstruction strategies, namely: the spectacle strategy of visual expression, the typification strategy of narrative subjects, and the striation strategy of character modelling. As an important genre of Chinese movies, war-themed themed movies often focus on the education and dissemination of national ideology, but “The Eight Hundred” broke the stereotype of this type of movie to the audience. Taking the film “The Eight Hundred” as an example, using the LDA model of computer image characteristics, combined with the related theories of visual effects, the thesis explores how the main theme of contemporary warfare movie content is expressed through images and its unique image style.
Research on the Application of Visual Maps Based on CiteSpace in Speech Technology
Authors: Yin Jiake 
Published: June 2021

Abstract
In recent years, deep learning has been widely used in the field of speech technology, and provides people with an efficient and convenient way of obtaining information and communicating in most application scenarios. This paper uses CiteSpace information visualization software to visually analyze the speech technology research literature based on nearly 10,000 papers in the field of speech technology research and application from 2013 to 2021. From the perspective of bibliometrics, this paper analyzes the distribution of hotspots and research frontiers in speech technology research in countries, institutions, disciplines and other information visualization maps, and compares and analyzes the literature information in the field of speech technology research and application at home and abroad in recent years. The research results show that the main disadvantages of speech technology at home and abroad are: the inability to completely avoid noise interference; the inability to correctly identify ambiguous and generalized sentences; and the inability to idealize the radio remotely. Aiming at the problem of noise interference, this paper proposes a deep learning-based noise reduction algorithm and increases the number of convolutions of the neural network to improve the model; then, for the ambiguity and generalization in the recognition process, this paper proposes a deep learning-based unsupervised training method; Finally, for the problems of sound reception and echo, this paper proposes a far-field sound pickup technology based on multi-channel signal microphone collection.
A Method of Brain Image Optimization based on an Autoencoder Unet
Authors: Chang Haipeng 
Published: June 2021

Abstract
As data and computer science grows rapidly in the modern era, numerous branches of technology appears one ofter another, medical imaging is one of them. After medical scans were first conducted as part of medical detection, prognosis, and treatment, methods such as CT, PET, and MRI scans. The precision of medical images is highly demanded for determination of lesion inside the human body. However, due the the variety of noise possibly taking place in the scan, and of how to prevent them each, it is merely impossible to avoid all images from being affected. A solution to this is denoising. Though many denoising methods had been leveled in the past decades, most traditional ones hold the possibilities of losing border information. In response to this specific issue, this paper will focus on using deep learning computation to generate a methods that is highly efficient and keeps images intact at the same time. This involves using unsupervised learning, convolutional neural network, U-net, and autoencode to create a process of the computer learning by itself to improve medical image qualities. With this accomplished, medical image quality can be thrown out of concern for doctors, lifting enormous weight of off their shoulder with the ever-growing population of patients.
Analysis of Wasp Transmission through Prediction Classification Model and Support Vector Machine
Authors: Wei Bao , Liu FangChao , Dong YuTing 
Published: June 2021

Abstract
Due to the hazards caused by Vespa Mandarinia, the public reports collected in Washing-ton State were classified. Therefore, two models were established in this paper: The Grey Pre-diction Model and the Prediction Classification Model. We begin with the Grey Prediction Model to predict the problem of whether a pest can spread over a period of time. Based on the gray scale prediction theory and combining with experts’ identification of the sighting report as the data of Vespa Mandarinia, GM (1,1) model has been established by using the gray scale prediction method.
Intelligent Information extraction algorithm of Agricultural text based on Machine Learning method
Authors: Wang Zihao , Cheng Zilong , Guan Chenzheng , Han Haoxiang 
Published: June 2021

Abstract
The Internet agricultural technology question and answer platform now only relies on manual to provide answer service, the response speed is slow, and the answer quality is difficult to be guaranteed. In order to realize the intelligent question and answer of agricultural technology and construct the knowledge base of agricultural technology, it is necessary to extract the named entity triple of “crop-pest-pesticide” from the existing question and answer data. There are few researches on agricultural Chinese named entity recognition, and the accuracy is low. According to the characteristics of named entities of crops, diseases and insect pests and pesticides, and according to the question and answer data of agricultural technology, a method of identifying named entities of crops, diseases and pests and pesticides based on conditional random field was proposed. The data set is formatted and segmented automatically, and the corpus after word segmentation is automatically tagged according to whether it contains a specific definition word, whether it contains a specific partial radical, whether it is a quantifier, whether it is a specific left and right definition word and part of speech. Using the tagged data to train the CRF model, we can classify the corpus, including judging whether the corpus belongs to crop, pest and pesticide named entities and identifying the position of the corpus in the compound named entity, thus realizing the recognition of the three kinds of named entities and automatically constructing the associated triple. Through the experiment to select the feature combination and adjust the context window size, the recognition accuracy of this method is improved, the model training time is reduced, and the accuracy of crop, pest and pesticide named entity recognition is 97.72%, 87.63% and 98.05%, respectively, which is significantly higher than the existing methods.
Multi-target Tracking Based on Deep Sort in Traffic Scene
Authors: Duan Chao , Li Xingxing 
Published: June 2021

Abstract
In this paper, we use the deep sort multi-target tracking algorithm to achieve multi-target tracking of pedestrians and vehicles in traffic scenes. In this paper, firstly, yolov4 is used to train the pedestrian and vehicle detection model in traffic scenes. Then, according to the detection frame predicted by yolov4, multi-target tracking is carried out for specific targets. The multi-target tracking algorithm uses deep Sort, which can be combined with yolov4, can achieve less ID switching in real-time reasoning and deal with the loss of occlusion, so as to achieve more stable tracking effect.
Computing Algorithm of Robot Distributed 3D Graphic Simulation System
Authors: Cao Junkai , Gao Zhijun , Han Zhonghua 
Published: June 2021

Abstract
In view of the shortcomings of current multi-robot path planning strategies, such as high path coupling, long total path, and long waiting time for collision avoidance, as well as the resulting problems of low system robustness and low robot utilization, the paper proposes Multi-robot path planning particle swarm algorithm based on distributed three-dimensional space-time graphics and motion decomposition. We first generate a dynamic temporary obstacle in the time dimension according to the existing path set and the current robot position, and expand it together with the static obstacle into a three-dimensional search graphics space. Secondly, in the three-dimensional image space, the total path movement time is divided into three parameters: movement time, turning time and staying time in place, and using the conditional depth-first search strategy to calculate all the matching parameters from the starting node to the target node the required path collection. The experimental results show that the path of distributed 3D graphics planning by our proposed particle swarm algorithm has the advantages of total length, less running time, system collision-free, and high robustness, which solves the problem of multi-robot systems completing continuous random tasks.
Kinematics Analysis of the 6-DOF Test-bed for the Sliding Bearing of Marine Gas Turbine
Authors: Li Xu , Zhang Gang , Yang Yumin 
Published: June 2021

Abstract
In order to solve the problem of simulating complex sea conditions on the sliding bearing test bed of marine gas turbines, a six-degree-of-freedom platform based on the Stewart platform is used to build the test bed. By reasonably arranging the positions of the hinge points on the upper and lower platforms, the inverse kinematics solution is used to calculate the length change of the six driving rods, finally, the simulation was verified by Matlab, which proved that the design is reasonable and feasible, and it can accurately simulate the complicated sea conditions encountered by marine gas turbines during sea navigation. Furthermore, it lays the foundation for further sliding bearing of marine gas turbines test and research.
Genetic Algorithm Based LQR Control for AGV Path Tracking Problem
Authors: Gu Jingming , Fang Dianjun 
Published: June 2021

Abstract
In order to study path tracking of AGV, the lateral dynamic model of AGV is established, and the state equation of the system is obtained. In order to make the state equation better adapt to the LQR controller, a lateral error integral is added to the controller. Then an improved state equation is obtained. The energy function is constructed and the LQR controller is designed. In order to find the optimal value of the weight Q and R in the LQR controller, Genetic Algorithm (GA) is introduced so as to realize the optimization of the LQR controller. The simulation results show that compared with the empirical method to determine the Q and R of LQR control, the LQR control method optimized by GA can effectively reduce the overshoot of the system, improve the convergence speed and stability of the system, and obtain a better path tracking effect.