bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Scheduling Based on Spatial and Temporal Distribution Characteristics of Piled Shared Bicycle
Authors: Ge Jing , Zhang Yi 
Published: August 2021

Abstract
With the development of bicycles, the problem of having no piles to park and no cars to use also appears. Because the driving mechanism of bicycle travel is not understood, the operation personnel in charge of redistribution have no way to start. In this paper, from the perspective of subway station and city bus transfer system, starting from the characteristics of road network, combining with the nature of land use, the spatial-temporal characteristics of quantitative analysis is carried out to study the spatial-temporal distribution law of travel behavior. Based on the above factors and the supporting conditions of the bicycle facilities, the common least square model was established. It was found that the number of stations in the traffic district was the most important variable, indicating that the basic supporting facilities had a great impact on the number of bicycle trips. Then, the number of bicycles and piles was deeply studied. Firstly, the stations are classified according to K-means, and defined as low-frequency high flow, high-frequency low flow, low-frequency low flow, and medium-frequency low flow according to the average of clustering results. Then, the redistribution suggestions are put forward respectively for these categories. For outflow clustering with large values of O and D, R can be predicted by BP neural network to provide reference for redistribution and distribution.
Bat algorithm for multilevel image thresholding based on Otsu and Kapur’s entropy
Authors: Yang Shengli , Chen Qiang , Peng Ling 
Published: August 2021

Abstract
Multilevel image thresholding has attracted plenty of attention in the past decades. Otsu and Kapur’s entropy-based methods are often applied to search the optimal bi-thresholding. These techniques are also suitable for multilevel thresholds. However, it takes a lot of computation to solve the multilevel threshold problem. To address this problem, in this paper, a recently proposed bat algorithm is used to find the appropriate multilevel thresholds, in which Otsu and Kapur’s entropy is regarded as its fitness functions. Evaluation of image segmentation effect is performed using the peak-to-signal ratio (PSNR) and structural similarity (SSIM) index. The experiment results show that Otsu based method is more suitable for multi-level threshold image segmentation.
Small sample vehicle target recognition method for unmanned aerial vehicle system based on deep learning
Authors: Zhang Tingping , Wan Di 
Published: August 2021

Abstract
Vehicle target detection technology refers to the process of vehicle detection and recognition in the image data set of different backgrounds by means of feature extraction. The vehicle target detection technology based on deep learning shows obvious advantages in the accuracy and speed of target detection. With the development of science and technology, the detection and recognition of vehicles in UAV aerial images has become an important applied research direction. This paper studies the detection and recognition of UAV aerial vehicle based on deep learning, and proposes a new deep learning-based algorithm to solve the problem that incomplete vehicle targets in the UAV aerial vehicle based on YOLOV3 algorithm cannot be recognized, and vehicles close to the UAV aerial vehicle are missed. Experimental verification results show that, compared with the existing algorithms, the proposed algorithm can significantly improve the detection accuracy of UAV aerial vehicle based on deep learning while ensuring real-time performance.
Research on the Realization and Application of Intelligent IoT Platform for Electrical Equipment under Industrial Internet
Authors: Bian Longjiang , Zhang Jingwei , Cui Qiang , Chen Xi , Wang Sheng 
Published: August 2021

Abstract
In the context of the industrial Internet, the new generation of information technologies such as the cloud, large, and mobile smart areas are becoming more mature and widely used. Intelligent manufacturing leads the transformation of manufacturing methods. The new development concept represented by “modern smart supply chain” is also getting more and more attention. Therefore, building a smart supply chain has become a new research direction, and building a smart IoT platform for electrical equipment is conducive to building a modern smart supply chain system. The current research on the intelligent IoT platform for electrical equipment is mainly in the platform structure design and key technologies, and there is a lack of implementation and application research on the intelligent IoT platform for electrical equipment. Therefore, this article will conduct research from the realization and application status of the intelligent IoT platform for electrical equipment, the problems in the implementation process, the key technologies and solutions used to solve the problems, etc., to provide a good basis for the construction and application of the intelligent IoT platform for electrical equipment. Provide reference for development.
InISAR Imaging of Maneuvering Target Base on Motion Compensation and Image Coregistration
Authors: Huang Jinhai , Zhou Jianjiang 
Published: August 2021

Abstract
The imaging of maneuvering target is the main research content of Interferometric Inverse Synthetic Aperture Radar (InISAR). Since it is quite difficult to obtain the effective data of the moving target, both the residual translational compensation and least square method are proposed to realize the imaging of the target according to the principle of InISAR in this letter. First step, the echo model is established to perform motion compensation. Second step, perform image registration on the signal. Third step, the ISAR image is obtained via extracting the peak value, then perform the interference processing imaging. Finally, Simulation results show that this method can be used for image recognition.
Research on dynamic target tracking algorithm in mobile sensor networks
Authors: Lu Shiwei 
Published: August 2021

Abstract
With the increasing complexity of networked cooperative detection environment, new targets appear and leave in the detection area, and the mobility of targets is increasing. The emergence of sensors makes the sensors in the sensor network have certain motion characteristics. These uncertainties increase the complexity of the problem. At the same time, the dynamic selection of sensor resources needs to consider the needs of different priority tasks, platform survival risk, sensor cost and other factors. In view of these problems to be solved, based on the qualitative synthesis theory of predictive control and stochastic dynamic programming optimization theory, the dynamic sensor selection decision theory and method based on multi-step forward decision are explored. This paper studies the dynamic sensor strategy based on multi-step forward decision scheduling, and carries out the research on the optimization decomposition method of sensor selection under different structural characteristics, the dynamic optimization method of sensor selection under complex and uncertain environment, and the multi-objective optimization method of dynamic sensor selection under heterogeneous targets, so as to explore a new way of dynamic sensor selection algorithm design and performance analysis under networked collaborative tracking A new way. The research of dynamic target tracking algorithm in mobile sensor networks has important theoretical and engineering significance to improve the resource efficient utilization and the overall target tracking performance of sensor networks.
Non-contact infrared temperature detection and RFID technology access control design
Authors: Zhu Zimin , Huang Yuehong , Chen Zhichao , Huang Hui 
Published: August 2021

Abstract
The non-contact infrared body temperature detection and RFID technology access control design uses 51 single-chip microcomputer as the core main control module, which combines infrared temperature measurement function (GY-MLX90614 sensor) and radio frequency (MFRC522) card swipe access control system, through swiping card and temperature measurement together control the door opening and closing. When it works in the high-risk zone mode, it detects the body temperature while swiping the card. If the body temperature is normal, the door opens, if the body temperature is abnormal, the door does not open and the buzzer sounds; when working in the low-risk mode, if the body temperature is normal, the door opens, If the body temperature is abnormal, the door opens and the buzzer sounds. Finally, it inputs the obtained card information and body temperature data to the WiFi circuit (ESP8266) through the serial port, and uses it as a hotspot to connect to the mobile device to realize data transmission, so as to realize more efficient unmanned management in different areas during the epidemic.
Design of zero position movement control and damping structure in infrared gun sight
Authors: Hongying Sun , Pu Ma , Zhenhao Duan 
Published: August 2021

Abstract
In this paper, starting from the key technical indicators of the infrared gun sight mirror, through the analysis of the cause of zero movement, on this basis, the design of zero movement control is carried out, and on the basis of ensuring that the zero movement momentum meets the index, the design of multiple damping structure is carried out. Through the field shooting test, the design method meets the requirement of zero movement momentum of 0.5mil. In terms of shock absorption, twenty thirty bad elements are reduced to 1-2 after the shock absorption, and obvious results are obtained. The feasibility and effectiveness of the design method mentioned in this paper in the design of zero position motion control and damping structure of infrared gun sight mirror are illustrated.
Application of Intelligent Technology in Electrical Engineering Automation Control
Authors: Huiyong Hu 
Published: August 2021

Abstract
Aiming at the problems of low control precision and complex control process in electrical engineering automation control, this paper proposes a kind of intelligent electrical engineering automation control technology based on DeviceNet. This paper focuses on the development process, communication protocol and object modeling method of DeviceNet universal I / O interface based on microprocessor. At the same time, this paper designs the application layer software, and gives the detailed hardware circuit and software design process. The experimental results show that the method can reduce the complexity of the control process and improve the control accuracy.
Optimization Model of Urban Rail Transit Operation Management Based on Neural Network Algorithm
Authors: Yang Liyuan 
Published: August 2021

Abstract
In order to improve the accuracy of urban traffic flow information and improve the efficiency of traffic control, this paper studies the application of artificial intelligence technology in urban traffic. This paper mainly introduces the intelligent transportation data acquisition and control system based on artificial intelligence and neural network architecture, and points out the trend of the integration of neural network technology and intelligent transportation. This paper designs an intelligent transportation data fusion method based on neural network, and applies it to real-time acquisition, inductive dynamic control, optimal path planning and so on. In this paper, the application of geographic information system (GIS), various intelligent sensors and electronic tag positioning technology in intelligent transportation system (ITS) is described in detail.
English Machine Translation Model Based on Artificial Intelligence
Authors: Li Xueting , Hao Xing 
Published: August 2021

Abstract
Machine translation is a language processing method that is widely used at present. However, because the machine translation mechanism extremely relies on rigid grammatical rules, the system often has large understanding deviations in the process of processing the original text, and it is often difficult to accurately grasp the semantic features. In this context, the author proposed an optimization method for English machine translation which is based on artificial intelligence on the premise of weakening grammatical rules. The above method first analyzes the grammar rules of English, and then obtains the semantic Gaussian marginalization rectangular window function. These functions project a large amount of information entropy data of English semantics. In order to obtain the nonlinear spectral features of adaptive matching semantics, the semantic correlation factors of words are added to the text information entropy and information gain. Simulation results show that the proposed method achieves good machine translation effect and has good development space.
Mechatronics Fault Prediction and Diagnosis Based on Multi Sensor Information Fusion
Authors: Yu Jinghua 
Published: August 2021

Abstract
This paper studies the mechatronics fault prediction and diagnosis based on multi-sensor information fusion. According to the method of data fusion, the fault diagnosis system is divided into data level fusion module, feature level fusion module and decision level fusion module. The data level fusion module mainly processes the measured signals of multi-sensor to extract the feature information of fault diagnosis. The feature level fusion module uses three parallel neural networks with the same structure. In this paper, the basic probability assignment method of decision level D-S evidence theory is used as the basic algorithm of data processing. The decision-making level uses the method of D-S evidence theory to fuse the results of feature level local diagnosis to get the final diagnosis result. The experimental data show that the accuracy and efficiency of the proposed Mechatronics fault prediction and diagnosis system based on multi-sensor information fusion can meet the requirements of engineering technology.
Adaptive preprocessing of character recognition image based on neural network
Authors: Wang Fang 
Published: August 2021

Abstract
Words in natural scenes contain abundant information. Automatic acquisition of text information in images can help people understand images more effectively and further process images such as storage, compression and retrieval. Pattern recognition technology is to use machines to simulate people’s various recognition abilities. At present, it mainly simulates people’s visual and auditory abilities. Based on BP neural network in deep learning, a text positioning BP neural network is proposed, which automatically extracts the text features in the image and avoids the defect of using manual design features This paper studies the preprocessing algorithm of Chinese character recognition in natural scenes, including binarization of text regions, extraction of character colors, clustering analysis of colors, segmentation of characters, etc., and designs the rules for merging text regions after positioning. Experimental results show that the algorithm can solve the problem of insufficient recognition ability for text images and long-sequence text images with complex background noise between characters when the background and text gray level are similar.
Application Data Mining Technology in Construction of College English Blended Teaching Mode
Authors: Wei Wei 
Published: August 2021

Abstract
With the opening up of China, the communication with the international community is increasing, and the technical talents who can adapt to the international trade are more and more eager. But at the same time, there are still some problems in English Teaching in higher vocational colleges, such as the lack of teaching time, the uneven English foundation of students due to different sources, and the low enthusiasm and fear of difficulties in English learning. Based on the practice design of mixed teaching mode, this paper analyzes the statistical results. According to the results, SIG. Value is 0.238, greater than 0.05, and sig. (bilateral) value is 0.047, less than 0.05. According to this significance level, we can know that there is a significant difference between the two classes. Therefore, compared with the traditional English teaching, SPOC teaching mode based on blended learning theory can improve students’ overall English level and learning efficiency.
An electronic component defect detection method based on SVM
Authors: Zhang Poxi , Qian Weiqi , Cai Hongbin , Lu Guanghui 
Published: August 2021

Abstract
In the production process of electronic components such as capacitors, bubble defects will occur inside the devices, which will lead to the loss of the functions of electronic components. Traditional methods for detecting bubble defects have many problems, such as low efficiency and low accuracy. Aiming at the bubble defects of the existing electronic components, an SVM-based detection method was proposed, which combined machine learning and image processing to complete the automatic detection of the bubble defects of electronic components. By using the feedback method, the error results obtained from the model classification are added to the training sample set, so that the problems of insufficient sample quantity and incorrect sample classification are solved. The accuracy of the proposed method was verified by capacitor component samples. The accuracy rate was 98.95%, the emission rate was 0.12%, and the error rate was 0.93%. It can be seen that the method proposed in this paper has a good effect on the defect detection of electronic components with the capacitor as an example.
Key Technology and Application of Computer Artificial Intelligence Recognition Based on Machine Vision
Authors: Wang Lihua 
Published: August 2021

Abstract
Computer artificial intelligence technology simulates human thinking and consciousness vividly by using the existing functions of computers, and displays various human thinking through computer programs. Artificial intelligence recognition technology belongs to the key technology in the field of computer artificial intelligence. With the rising level of science and technology, this technology has developed rapidly and has been applied to many fields of society. Intelligent recognition technology is computer system, scanning equipment, camera technology, etc., which can intelligently recognize target instructions and data information, so as to meet the needs of intelligent recognition in the current era. In this paper, the problems faced in the application of computer artificial intelligence recognition technology are studied, and the key technologies of computer artificial intelligence recognition based on machine vision are analyzed, trying to find ways to make the application of computer artificial intelligence recognition technology get out of the bottleneck and remove obstacles to further promote the development of computer artificial intelligence technology.
Conceptual Design of Mechatronics System Based on Intelligent Control
Authors: Li Jinming , Liu Lining 
Published: August 2021

Abstract
With the development of society, the research on product concept design has become more and more mature, and the conceptual design of mechatronics system has gradually become the key research and design direction in the field of mechatronics. The rapid development of modern industrial production not only provides strong support for the application and popularization of mechatronics system, but also promotes the overall improvement of industrial production efficiency and quality. Electromechanical integration is a comprehensive mechanical system in essence. Because the traditional industrial control technology can no longer meet the requirements of industrial production on the function of mechatronics system. Intensify the research and application of intelligent control technology, minimize the impact of human factors on industrial production, and promote the effective improvement of the operation performance of mechatronics system. This paper analyzes the application of intelligent control in mechatronics system, and discusses the conceptual design method of mechatronics system based on intelligent control.
Research on Key Technologies of Robot for Visual Detection of Foreign Matter in Liquid Medicine Based on Machine Learning Algorithm
Authors: Liang Qilang 
Published: August 2021

Abstract
At present, in the pharmaceutical industry, people pay more and more attention to the quality of drugs, and therefore put forward higher requirements for the detection of drugs. In this paper, based on a large number of theories and experiments, an intelligent on-line detection system for visible foreign bodies in medicated liquor solution is designed based on machine learning algorithm. From the angle of mechanics and kinematics, the force analysis of moving objects in liquid after high-speed rotation and sudden stop is discussed, which provides an important reference standard for distinguishing visible foreign objects from random noise. The experimental results show that the algorithm can effectively eliminate the vibration interference of electromechanical system, and make the foreign body detection equipment of liquid medicine successfully migrate from single-station detection equipment to automatic online detection robot, which is of great significance to the research and development of the whole equipment.
Analysis and Synthesis of Human Motion Function Data Based on Decision Tree Classification
Authors: Li Ying 
Published: August 2021

Abstract
At present, motion capture is widely used in computer animation, games, movies and robots, but it is still a difficult problem to synthesize stylized human motion. To solve this problem, a motion synthesis method based on decision tree classification and block principal component analysis is proposed. Block principal component analysis is carried out on the motion data grouped according to the characteristics of human skeleton structure, and low-dimensional subspace parameters with specific semantics are obtained. Triangular constraint is used to block the connection between moving frames which are far apart, thus ensuring the time sequence continuity of segmentation results; In the retrieval process, the similarity of key points is calculated according to different influence degrees in turn; Finally, an efficient motion retrieval simulation system is realized.
An Intelligent Threshold Alarm Model Based on Abnormal Judgment of Logic Indicators
Authors: Huang Jianhua , Liu Xinlin 
Published: August 2021

Abstract
In order to improve the information sensitivity of power transmission and transformation environment, this paper establishes the threshold alarm model of condition monitoring data of power transmission and transformation equipment based on the abnormal judgment method of business logic index. This paper extracts the necessary alarm transaction of condition monitoring data of power transmission and transformation equipment. According to the extraction results, the specific data level of authority threshold is determined by means of data cleaning, and the smooth application of power transmission and transformation equipment condition monitoring data threshold alarm model is realized. The experimental results show that, compared with the local state processing model, the accuracy of directional electronic transfer is more than 75% and the threshold can reach the standard authority threshold level. The problem of low sensitivity of original power transmission and transformation information has been effectively solved.