bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Corner Detection Algorithm Based on Regional Center of Mass in Imaging through Water Surface
Authors: Mai Changli , Jian Bijian , Ling Yongfa 
Published: December 2021

Abstract
Structural light active imaging can obtain more information about the target scene, which is widely used in image registration,3D reconstruction of objects and motion detection. Due to the random fluctuation of water surface and complex underwater environment, the current corner detection algorithm has the problems of false detection and uncertainty. This paper proposes a corner detection algorithm based on the region centroid extraction. Experimental results show that, compared with the traditional detection algorithms, the proposed algorithm can extract the feature point information of the image in real time, which is of great significance to the subsequent image restoration.
Research on Network Platform System Under Mobile Internet and Smart Electronic Commerce Big Data Digitization
Authors: Li Bin , Zhou Zhisheng 
Published: December 2021

Abstract
Based on the intelligent big data method and the cross-border e-commerce logistics service system as the research object, the paper constructs a cross-border e-commerce logistics service system based on mobile Internet. According to the user’s preference, the products matching the user’s preference are obtained through association rule mining, and recommended to the user. The research shows that the digital cross-border e-commerce platform system involved in the paper has good compatibility, and the user preference recommendation algorithm involved in the paper has high accuracy, which meets the needs of users.
Multi-Modal Remote Sensing Image Matching Method Based on Deep Learning Technology
Authors: Han Hao , Li Canhai , Qiu Xiaofeng 
Published: December 2021

Abstract
Remote sensing is a scientific technology that uses sensors to detect the reflection, radiation or scattering of electromagnetic wave signals from ground objects in a non-contact and long-distance manner. The images are classified by the extracted image feature information Recognition is a further study of obtaining target feature information, which is of great significance to urban planning, disaster monitoring, and ecological environment evaluation. The image matching framework proposed in this paper matches the depth feature maps, and reversely pushes the geometric deformation between the depth feature maps to between the original reference image and the target image, and eliminates the geometric deformation between the original images. Finally, through feature extraction of the corrected image, the extracted local feature image blocks are input into the trained multi-modal feature matching network to complete the entire matching process. Experiments show that the negative sample set construction strategy that takes into account the sample distance proposed in this experiment can effectively deal with the problem of neighboring point interference in RSI matching, and improve the matching performance of the network model.
The Parameter Estimation of Double Exponential Pulse Based on a Least Infinity Norm Fitting Method
Authors: Shi Yuewu , Wang Wei , Zhu Zhizhen , Nie Xin 
Published: December 2021

Abstract
This paper presents an estimation method of double exponential pulse (DEP) between the physical parameters rise time (t
r), full width at half maximum amplitude (t
FWHM) and the mathematical parameters α, β. A newly fitting method based on the least infinity norm criterion is proposed to deal with the estimation problem of DEP. The calculation process and equation of parameters of this method is proposed based on an m-th-order polynomial fitting model. This estimation method is compared with the least square method by the same data and fitting function. The results show that the maximum estimation error of parameters of double exponential pulse obtained by the least infinity norm method is 1.5 %.
Research on Fan Operation Evaluation and Error State Judgment Relying on Improved Neural Network and Intelligent Computing
Authors: Liu Xueyi , Dong Junhao , Tu Guangyu 
Published: December 2021

Abstract
Fan, as the most commonly used mechanical equipment, is widely used. In order to solve the problem of fan bearing fault diagnosis, this paper analyzes the main factors affecting fan spindle speed and power generation in operation. The input and output parameters of the performance prediction model are determined. The performance prediction model of wind turbine is established by using generalized regression neural network, and the smoothing factor of GRNN is optimized by comparing the prediction accuracy of the model. Based on this model, the sliding data window method is used to calculate the residual evaluation index of wind turbine speed and power in real time. When the evaluation index continuously exceeds the pre-set threshold, the abnormal state of wind turbine can be judged. In order to obtain wind turbine blades with better aerodynamic performance, a blade aerodynamic performance optimization method based on quantum heredity is proposed. The B é zier curve control point is used as the design variable to represent the continuous chord length and torsion angle distribution of the blade, the blade shape optimization model aiming at the maximum power is established, and the quantum genetic algorithm is used to optimize the chord length and torsion angle of the blade under different constraints. The optimization results of quantum genetic algorithm and classical genetic algorithm are compared and analyzed. Under the same parameters and boundary conditions, the proposed blade aerodynamic optimization method based on quantum genetic optimization is better than the classical genetic optimization method, and can obtain better blade aerodynamic shape and higher wind energy capture efficiency. This method makes up for the shortcomings of traditional fault diagnosis methods, improves the recognition rate of fault types and the accuracy of fault diagnosis, and the diagnosis effect is good.
Research on the Application of Artificial Intelligence Machine Learning Technology in Improving the Accuracy of Engineering Image Processing
Authors: Liu Xiaowen , Lei Juncheng 
Published: December 2021

Abstract
Image recognition technology mainly includes image feature extraction and classification recognition. Feature extraction is the key link, which determines whether the recognition performance is good or bad. Deep learning builds a model by building a hierarchical model structure like the human brain, extracting features layer by layer from the data. Applying deep learning to image recognition can further improve the accuracy of image recognition. Based on the idea of clustering, this article establishes a multi-mix Gaussian model for engineering image information in RGB color space through offline learning and expectation-maximization algorithms, to obtain a multi-mix cluster representation of engineering image information. Then use the sparse Gaussian machine learning model on the YCrCb color space to quickly learn the distribution of engineering images online, and design an engineering image recognizer based on multi-color space information.
Research on underwater image enhancement algorithm based on improved DCP
Authors: Wu Zhe , Han Jianfgui , Cao Chenghao 
Published: December 2021

Abstract
All for underwater images, there are some drawbacks, such as low definition, serious color bias, dark brightness, etc. On the basis of in-depth analysis of common image enhancement algorithms, This paper uses the improved dark channel priority algorithm to enhance the underwater image, Improving the contrast of underwater images and color correction of underwater images. Color correction is added based on dark channel prior algorithm; Make the image look more even, higher contrast, more acceptable. The improved algorithm model has a higher transfer rate; PSNR is more balanced and has better contrast to meet the requirements of underwater image observation.
Comparison and intelligent analysis of NTRU and ETRU signature algorithms for public key digital signature
Authors: Zhu Zifeng , Tian Fei 
Published: December 2021

Abstract
Three American mathematicians made the NTRU public-key cryptosystem in 1996, it has a fast speed, small footprint, and also it is easy to produce key advantages. The NTRU signature algorithm is based on an integer base, the performance of the signature algorithm will change when the integer base becomes other bases. Based on the definition of “high-dimensional density” of lattice signatures, this paper chooses the ETRU signature algorithm formed by replacing the integer base with the Eisenstein integer base as a representative, and analyzes and compares the performance, security of NTRU and ETRU signature algorithms, SVP and CVP and other difficult issues, the speed of signature and verification, and the consumption of resources occupied by the algorithm.
Research on Key Technologies of FDM 3D Printing Based on Computer Assisted 3D Reconstruction
Authors: Gao Zihang , Zhao Nannan 
Published: December 2021

Abstract
As a core technology in the field of high and new tech manufacturing,3D printing occupies an important position in the production process of aviation, shipbuilding, and automobiles. With the rapid development of 3D printing basic theory in recent years, how to further improve product accuracy, quality and modeling efficiency has become the current research focus in this field. Firstly, we conducted further research on the key technologies of FDM 3D printer, and combining 3D reconstruction technology with pretreatment technology. Secondly, we constructed and optimized the corresponding model layered slice and filling path planning algorithm. Thirdly, we designed a 3D suitable for rapid prototyping products printing control system. The research can provide an effective experience for the application of 3D printing technologies.
Material selection and optimization scheme of Seabed Data Center
Authors: Xu Kechao , Gu Xiaotong , Jiao Shangyi , Ren Boyang , Tian Zhengyu , Fan Yutong , An Yan , Lin Yuhan , Wang Hanxiao 
Published: December 2021

Abstract
With the advent of the era of big data, the number of global data centers increases rapidly. But at the same time, it also brings some conflicts of resources. Because electronics use a lot of energy to dissipate heat. In this paper, In order to select the most suitable materials for Seabed Data Center, we established a material screening model based on weight and entropy weight method, and finally obtained the optimal design scheme.
Path planning method of smart mobile robot based on genetic algorithm
Authors: Fu Yan 
Published: December 2021

Abstract
Robot path planning is an important topic in the field of robots. Unlike previous methods which consider the smoothness of paths in the process of genetic algorithm, this paper presents a new method of robot path planning that separates the genetic algorithm process from the path smoothing process. First, a simple genetic algorithm with variable length encoding is designed to generate a better polyline path, and then a new type of spiral with shape parameters is introduced to smooth it to smooth a larger corner. Throughout the path planning process, only obstacle coordinates are input to select parameters adaptively to generate the robot’s walking path. The simulation results show that separating the genetic algorithm process from the path smoothing process reduces the complexity of the genetic algorithm itself, so the designed smoothing operation not only improves the smoothness of the path, but also reduces the length of the path.
Application of Big Data in Security Precaution by Network Technology and Video Image Database
Authors: Yao Yougang , Zou Linling 
Published: December 2021

Abstract
The security industry is generated by the needs of modern public security precaution. With the popularization of hardware and network technology, the amount of data in the field of security increases rapidly. Under such a condition, big data technology in the field of security arises. This paper discusses the current situation of the security industry, and discusses the related security problems in the application process of big data in the field of public security precaution. Taking the video image database application platform as an example, combined with the national standards 28181, 25724, 35114, this paper discusses the data security problems involved in the process of data acquisition and data transmission. Based on the Kerberos authentication mechanism of Hadoop, this paper introduces the big data security and access control technology, and forms relevant solutions in the whole process of data acquisition, data transmission and data access.
Research on Vehicle Scheduling of Cross-Regional Collection Using Hierarchical Agglomerative Clustering and Algorithm Optimization
Authors: Wei Zeming , Liang Chufeng , Tang Hua 
Published: December 2021

Abstract
At present, municipal solid waste (MSW) collection is based on the divide-regional operation mode, which has many deficiencies. This paper proposes a cross-regional operation scheme. Through the initial assignment, type labeling, and reassignment, and use the improved hierarchical agglomerative clustering (IHAC) algorithm and garbage collecting route optimization (GCRO) algorithm to realize intelligent allocation of garbage and scheduling route planning of collection vehicles. The experimental results demonstrate the proposed scheme improves the utilization of vehicle resources, reduces the operating cost, realizes the balanced allocation of garbage, and solves the problems caused by limitations of the original operation scheme, which demonstrates the feasibility and effectiveness of the cross-regional operation.
ERDBNet: Enhanced Residual Dense Block Net --A New Net to Rich ESRGAN Image Details
Authors: Gao Lizhuo 
Published: December 2021

Abstract
Super resolution is applied in many digital image fields. In many cases, only a set of low-resolution images can be obtained, but the image needs a higher resolution, and then SR needs to be applied. SR technology has undergone years of development. Among them, SRGAN is the key work to introduce GAN into the SR field, which can truly restore a large number of details on the basis of low-pixel pictures. ESRGAN is a further improvement on SRGAN. By removing the BN layer in SRGAN, the effect of artifacts in SRGAN is eliminated. However, there is still a problem that the restoration of information on small and medium scales is not accurate enough. The proposed ERDBNet improve the model on the basis of ESRGAN, and use the ERDB block to replace the original RRDB block. The new structure uses a three-layer dense block to replace the original dense block, and a residual structure of the starting point is added to each dense block. The pre-trained network can reach a PSNR of 30.425 after 200k iterations, and the minimum floating PSNR is only 30.213. Compared with the original structure, it is more stable and performs better in the detail recovery of many low-pixel images.
A New Knowledge Distillation Method for Object Detection Based on EMD
Authors: Liang Zhihao 
Published: December 2021

Abstract
As a common method of model compression, the knowledge distillation method can distill the knowledge from the complex large model with strong learning ability to student small model with weak learning ability in the training process, to improve the accuracy and performance of the small model. At present, there has been much knowledge distillation methods specially designed for object detection and achieved good results. However, almost all methods failed to solve the problem of performance degradation caused by the high noise in the current detection framework. In this study, we proposed a feature automatic weight learning method based on EMD to solve these two problems. That is, the EMD method is used to process the space vector to reduce the impact of negative transfer and noise as much as possible, and at the same time, the weights are allocated adaptive to reduce student model’s learning from the teacher model with poor performance and make students more inclined to learn from good teachers. The loss (EMD Loss) was redesigned, and the HEAD was improved to fit our approach. We have carried out different comprehensive performance tests on multiple datasets, including PASCAL, KITTI, ILSVRC, and MS-COCO, and obtained encouraging results, which can not only be applied to the one-stage and two-stage detectors but also can be used radiatively with some other methods.
UAV formation obstacle avoidance control method based on artificial potential field and consistency
Authors: Wei Boyu 
Published: December 2021

Abstract
As a typical multi-agent formation, UAV formation is playing an increasingly powerful role in the civilian and military fields. Obstacle avoidance, as an important technology in controlling formation, determines the application prospects of UAVs. This paper studies the time-varying formation of UAVs with interactive topology to avoid obstacles, aiming to improve the ability of UAV formations to deal with complex environments while traveling. Firstly, a repulsive force field is reasonably introduced based on the existing control scheme, and an improved distributed time-varying formation control scheme based on artificial potential field is proposed. Then combined with the basic idea of model predictive control, an obstacle avoidance strategy in which UAV obstacle avoidance and formation shaping are carried out simultaneously is proposed. Finally, a time-varying formation simulation experiment containing four UAVs was carried out to verify the validity of the results.
Application of Information Technology and Artificial Intelligence in Computer Assisted Costume Design
Authors: Jing Fei 
Published: December 2021

Abstract
Costume Design and information technology can be used as a collision between fashion and information technology. As a course that combines artistry and technology, costume and costume design have higher and higher teaching requirements for technical environment on the basis of art display, and attaches equal importance to students’ innovative thinking and practical ability in the teaching process. Compared with traditional teacher-centered, only attach importance to knowledge and neglecting the students’ ability development of teaching mode can meet the demand for professional talents, and reasonable use of the information technology as the guide, give full play to student’s main body role, respect students’ individual differences, according to the diverse learning needs of students, In order to better train out of the garment industry needs high-quality talents.
Research on Computer Numerical Simulation System and Information Digitization Using Competition and Confrontation Network
Authors: Wang Liangyu 
Published: December 2021

Abstract
The competitive characteristics and training requirements of each event group that can dominate the category of difficult and beautiful performance of technical warfare have multi-dimensional similarities. From the aspects of competition characteristics, determinants of sports performance, structural characteristics of competitive ability, and training requirements, it analyses the antagonistic event group that can dominate the class of technical warfare, and puts forward corresponding training requirements. The development of computer simulation technology of human body system will undoubtedly bring unprecedented opportunities to sports training and sports science research, and is changing the mode of sports science research and training. The article uses the confrontation network to conduct competitive sports simulation training, which can carry out the scientific selection of competitive sports athletes, improve the overall training level and effect of competitive sports athletes, optimize, and innovate the technical movement and movement combination arrangement of competitive sports, and can truly construct competitive sports training scenes.
Path Planning Algorithm for Mobile Robot Based on Improved Ant Colony Algorithm
Authors: Li Yanyun , Liu Fenggang 
Published: December 2021

Abstract
Due to the influence of full traversal environment, the path length obtained by existing methods is too long. In order to improve the performance of path planning and obtain the optimal path, a full traversal path planning method for omnidirectional mobile robots based on ant colony algorithm is proposed. On the basis of the topology modeling schematic diagram, according to the position information of the mobile robot in the original coordinate system, a new environment model is established by using the Angle transformation. Considering the existing problems of ant colony algorithm, the decline coefficient is introduced into the heuristic function to update the local pheromone, and the volatility coefficient of the pheromone is adjusted by setting the iteration threshold. Finally, through the design of path planning process, the planning of omnidirectional mobile robot’s full traversal path is realized. Experimental results show that the proposed method can not only shorten the full traversal path length, but also shorten the time of path planning to obtain the optimal path, thus improving the performance of full traversal path planning of omnidirectional mobile robot.
Path planning of intelligent mobile robot based on Dijkstra algorithm
Authors: Li Xueyan 
Published: December 2021

Abstract
Under the premise of grid environment modeling method, a relaxed Dijkstra algorithm is proposed to solve the problem of real-time path planning for mobile robot in large-scale and obstacle intensive working environment. Firstly, four neighborhood search is used to construct the Manhattan distance potential field from the source point to the global point in linear time, and then eight neighborhood search is performed from the target point to the source point to return a collision free and approximately optimal path. Matlab simulation results show that the algorithm is 10 times faster than dijksta algorithm and A-star algorithm, and the error of path length is within a reasonable range compared with the shortest path.
Research And Analysis of Nonlinear Identification Adaptive Control of Fitness Using MATLAB Numerical Simulation
Authors: Gou Xiaoping , Zhang WanJun , Zhang Feng , Qi Yu , Zhang Wenlan , Zhang WanLiang , Li Yadong 
Published: December 2021

Abstract
Aiming at the problems of non-linearity in physical exercise, difficulty in predictive model identification control, and difficulty in exercise in control target. A nonlinear identification adaptive control algorithm based on physical exercise is proposed, and a nonlinear identification adaptive control system based on physical exercise is established and simulated with MATLAB. Simulation results show that the recognition ability of physical exercise is strong, which improves the requirements of physical exercise quality, and has the advantages of simple control algorithm, high control accuracy and strong anti-interference ability.