bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design of intelligent distribution network based on Geographic Information Technology
Authors: Zhang Linyao , Hu Zhenda , Fang Xiaojie , Xu Ye , Cai Xiuwen , Chen Shu 
Published: January 2021

Abstract
According to the business requirements of distribution network planning, this paper deeply studies the existing simplified display algorithm of distribution network in GIS, constructs the distribution network model based on geographic information technology, develops the functions of supporting storage, display and display, and the function module of professional map editing for distribution network planning. The concept of spatiotemporal data and its application in GIS are innovatively put forward. The display research based on the status quo and the analysis and management technology of distribution network structure under planning state are carried out. The spatiotemporal representation, spatiotemporal evolution, and comparison of power grid are realized. The geographic image elements are matched and associated with equipment parameters to realize the integrated modeling of map and power grid, realize the penetration function from the map to equipment parameters and power grid indicators, and support the diagnosis and analysis of power grid-related indicators. Finally, a three-dimensional power map with time dimension, space dimension, and attribute dimension is constructed to provide geographic environment and equipment parameter data for power grid planning, site selection, and line selection.
Research on Spatial Positioning System of Fruits to be Picked in Field Based on Binocular Vision and SSD Model
Authors: Zhang Xing , Gao Qiaoming , Pan Dong , Cao Peng Cheng , Huang Dong Hui 
Published: January 2021

Abstract
The accurate fruit recognition in the field was one of the key technologies of fruit picking agricultural robots. An improved Single Shot Multi-Box Detector (SSD) model based on the color and morphological characteristics of fruit was proposed in this paper when aimed at the large collection workload and low secondary transfer efficiency of fruit such as palm fruit, durian, pineapple and other fruits grown in a complex field environment. A binocular depth camera RealSense D435i was used to collect images of the fruit to be picked in the field. Meanwhile, the MobileNet was replaced with the VGG16 basic network based on the Tensor-flow deep learning framework to reduce the amount of convolution operations for extracting image features in the SSD model, and a spatial positioning system for pineapple fruit was designed. Furtherly, experiments showed that the improved SSD depth detection model had a smaller size and it was more convenient to be deployed on the mobile end of agricultural robots, which the model had a high accuracy in the effective recognition of the fruits to be picked under the weed occlusion and overlapping scenes. The frame rate of the video reading and detection for the binocular depth camera reached 16.74 Frames Per Second (FPS), which had good robustness and real-time, and a good solution for the automatic picking of agricultural picking robots could be provided in the field.
A Face Detection Method Based on Skin Color Features and AdaBoost Algorithm
Authors: Yan He , Liu Yuhan , Wang Xiaotang , Li Mengxue , Li Huan 
Published: January 2021

Abstract
The face detection based on skin color features is greatly affected by illumination and the face detection based on AdaBoost algorithm is greatly affected by multi-pose of face, and this means that the large face deflection will make the detection less accurate. So we investigated a face detection method that combines the skin color model and the improved AdaBoost algorithm in this paper. This method first judges whether the collected face image needs illumination compensation. The image is converted from RGB color space to YCbCr color space with higher pixel clustering characteristics and distribution rules by whether it is needed or not. Meanwhile, the candidate regions containing face images are carried out through morphological process and shape select after establishing the Gaussian model. Finally, the face detection results are obtained through detecting the candidate regions with face images by improved AdaBoost algorithm. A large number of experimental results show that this new method can effectively reduce the false detection rate and improve the efficiency of face detection.
Research on Recognition Method of Car Fuel Tank Cap Based on Residual Network
Authors: Zhang Qingpeng , Li Yongqing 
Published: January 2021

AbstractAiming at the problem that traditional algorithms have low recognition accuracy for different car fuel tank caps, a car fuel tank cap recognition algorithm based on improved Faster-RCNN is proposed. Firstly, the VGG16 network in the feature extraction network link is improved to the ResNet-101 residual network to improve the recognition accuracy and system speed. Secondly, the ROI pooling of the original pooling layer is changed to ROI align regional feature aggregation method makes the target rectangular frame more accurate. Finally, the fully connected network is used to perform the classification of the car fuel tank cap and the accurate regression of the frame. Through the detection of 200 actual images of car fuel tank caps, the experimental results show that the algorithm can achieve 98.0% accuracy and a detection speed of 0.139 seconds per image. Compared with traditional image algorithms, it can obtain faster speed and higher accuracy, and can provide technical support for unmanned gas stations, which has certain engineering application value.
Design and Implementation of Lossless Compression System for CCSDS Hyperspectral Images
Authors: Zhang Lili , Liu Tao , Zhang Kechun 
Published: January 2021

AbstractBased on the xc7k325tffg900 FPGA chip of Xilinx Company and combining the actual hyperspectral image compression task requirements, the calculating speed and compression efficiency of the algorithm module are optimized. In the design of the prediction module in this article, the two-clock solution is used to solve the problem that the algorithm speed is restricted due to the feedback of the weight update. And the RTL-level design is implemented on the FPGA. CCSDS 123.0-B-1 hyperspectral image lossless compression algorithm is achieved. The system uses block compression to deal with the data source to avoid the entire system compression data errors caused by a certain pixel compression error. So the system’s Error Resilience ability is improved. Finally the compressed code stream is decoded. The decompressed image is consistent with the raw one, which verifies the correctness of this design.
Micro-mechanism of low-density lipoproteins (LDL) transport in multi-layered arterial wall
Authors: Wang Erhui , Zhang Xuelan , Suleiman Kheder , Shu Chang , Zheng Liancun 
Published: January 2021

Abstract
This paper investigates micro-mechanism of LDL transport numerically in a multi-layer arterial wall. A novel comprehensive 3D model is developed by considering the coupling interactions between vascular layers, and numerical simulations are carried out for a two-layer structure arterial wall. Results displayed the effects of dynamic viscosity on LDL transport. Moreover, we present data demonstrating that endothelial cells have a great blocking effect on the permeation and transmission of macromolecules by the different vascular wall. Furthermore, to verify the reliability and efficiency, a comparison is presented which shows that our numerical results are in good consistency with the experimental data in the literature.
Design and Implementation of Waste Reduction Intelligent Cafeteria System Based on Raspberry Pi
Authors: Zhang Shuai , Wu Qiwen , Zhu Tianyin , Hou Keyu 
Published: January 2021

Abstract
With the development of higher education in China, the number of college students on campus is increasing, and the number of people eating in college cafeterias also increases. On the one hand, the problem of crowded lines in school cafeterias during peak dining periods is becoming more and more serious, and students have a poor dining experience. On the other hand, it is often difficult to establish an effective communication mechanism between students and cafeterias. The traditional canteen model is difficult to meet the current needs, and the upgrade of the canteen system in universities is urgent. This paper designs a set of waste reduction intelligent cafeteria system based on Raspberry Pi to reduce waste. The whole system effectively solves the problems of traditional canteens, and establishes a certain reward mechanism to encourage students to use up the purchased dishes and reduce waste. The university canteen provides a practical model for improving service quality and reform and transformation.
Design of Networked Ammunition Network Communication Scheme Based on ESP8266
Authors: Zhang Xuhuan , Li Haojie , Huang Hu , Feng Yue 
Published: January 2021

Abstract
This paper analyzes the collaborative combat process of networked ammunition and forms a complete expression of the functional requirements and design constraints of the networked ammunition network communication scheme. Based on the requirements for the wireless communication function of the fuze by the network cruise missile in each process, the article proposes a network node communication networking scheme based on ESP8266, and conducts a hardware construction test. After testing and verification, the network system can realize stable information transmission between different nodes and between network nodes and the host computer. This networked ammunition system also has the functions of real-time reporting of node status, data encoding and decoding, and has low power consumption and stable performance. It is suitable for application in networked ammunition combat systems.
Design of Character Recognition Robot Based on FPGA
Authors: Zhu Y , Tang Y F , Wu Y X 
Published: January 2021

Abstract
As the inspection tour targeted at disarranged parking phenomenon conducted by specially assigned security personnel to parking lots in neighborhoods makes it more difficult for parking management, this paper proposes an FPGA-based character recognition robot design. The camera was controlled by the master controller EP4CE115F29C7N to acquire the license plate information, the improved character recognition algorithm based on YCbCr color space was put forward to identify the license plates, and the robot walking was controlled by combining IR control with increment-type PID. The experimental results show that this robot is of certain practical value as it is capable of effective recognition and displaying the characters on the license plates.
Study on User Experience of Live Streaming Sales Based on ISM and Kano Quality Model
Authors: Feng Yu-Liang , Huang Chia-Hui 
Published: January 2021

Abstract
With the development of the Internet economy, live streaming sales have become a new marketing method, which is popular among users. This paper explores quality attributes of live streaming sales based on the experience of their users. In this study, 56 factors, including the original and abstract reasons for and the specific item of user experience of live streaming sales, were first obtained with the evaluation grid method according to the semi-structured interview of highly involved people. Then, 5 experts formed a focus group for discussion and determined 11 key factors influencing user experience as analysis factors of the ISM theoretical model. Results are obtained through the statistical analysis of the ISM theoretical model, and four major dimensions are defined, namely the first-level demand dimension, the second-level interaction dimension, the third-level good quality dimension and the fourth-level extension dimension. Finally, the relationship between attractive quality attributes in different dimensions and the Kano two-dimensional quality classification and user satisfaction is analyzed based on the Kano two-dimensional quality questionnaire. A satisfaction coefficient matrix diagram is drawn to analyze the key quality attributes. Research conclusions and suggestions are made according to the results of data analysis so as to provide reference value for subsequent improvement of related designs and help designers improve user experience.
Application of Improved YOLO V3 Algorithm for Target Detection in Echo Image of Sonar under Reverb
Authors: Fang Jie , Wang Pingbo 
Published: January 2021

Abstract
aiming at the problems of low detection rate and high false alarm rate of small and medium targets in images, a YOLO V3 improvement method is proposed and applied to the detection of small targets. Because the small target occupies less pixels and the feature is not obvious, it is proposed to up-sample the 8 times downsampling feature map of the original network output, splice the 2 times upsampling feature map with the second residual block output feature map, and establish the feature fusion target detection layer with 4 times downsampling output. In order to gain
More small target feature information is added to the second residual block DarkNe-t53 the YOLO V3 network structure. k-means clustering algorithm is used to cluster the number of target candidate boxes and the aspect ratio dimension. The results show that the improved YOLO V3 algorithm can effectively detect small targets and improve the recall rate and detection rate of small targets.
Deep learning SPECT lung perfusion image classification method based on attention mechanism
Authors: Zeng Sitao , Cao Yongchun , Lin Qiang , Man Zhengxing , Deng Tao , Wang Rong 
Published: January 2021

Abstract
SPECT lung perfusion is an important functional imaging technology. It can capture the functional lesions of the lung in a non-invasive manner and has become an important clinical detection method for diseases such as pulmonary embolism. In order to realize the automatic detection of the degree of pulmonary embolism, this paper studies and constructs a deep classification model based on the attention mechanism. First, the normalization technique is used to convert the original lung perfusion file into a SPECT image; secondly, in view of the over-fitting phenomenon of the deep learning model caused by the small amount of medical image data and the unbalanced data, the image translation and rotation techniques are used to perform effective expansion; then, in order to improve the model’s feature extraction ability, the attention mechanism is combined with the depth classification model to build a SPECT lung perfusion image classification model; finally, a set of real SPECT lung perfusion images were used to carry out comparative experiments on various depth classification models. The experimental results show that the model proposed in this paper can effectively detect the extent of lung disease lesions, and the classification accuracy rate exceeds 88%, which verifies the effectiveness and reliability of the classification model.
Imbalanced Thangka Image Classification research Based on the ResNet Network
Authors: Zeng Fuliang , Hu Wenjin , He Guoyuan , Yue Chaoyang 
Published: January 2021

Abstract
Aiming at the problem of performance degradation caused by ignoring the softmax score of the wrong class in the training process of the unbalanced data set of Thangka images and the problem of the loss of negative feature information in the propagation process of the ReLU activation function, a new loss calculation method is proposed. Firstly, the parameters of the pre-training model on the COCO data set are used as the initial parameters. Secondly, CE and CCE are used to calculate the loss during the calculation of loss in the back propagation. Finally, AReLU activation function is used and a weight assigned to CE and CCE is added as final loss to update the parameters. The experimental results show that this algorithm improves the convergence speed and accuracy of the model with respect to imbalanced data. Compared with other loss functions, ours method performance is state-of-the-art, such as complement cross entropy.
Illumination-Adapted Long-Term Tracking Based on Multiple Correlation Framework
Authors: Han Xikai , Yu Gang , Hu Han , Liu Jia 
Published: January 2021

Abstract
Object tracking is a hot field in image processing, among which the kernel correlation filtering algorithm is widely applied based on the advantages of precision and speed. However, due to information decay, it has poor performance in the long-term tracking. In this paper, we develop an illumination-adapted long-term tracking method based on correlation filtering tracker. Firstly, we introduce illumination adaptive normalization and information entropy weighted feature fusion to improve the prediction accuracy. Secondly, we estimate the object scale quickly based on the binary strategy to improve the speed of the algorithm. Then, we design re-detection based on online SVM detection for target loss. Finally, we design an appropriate updating mechanism to combine the whole tracking framework with various judgment thresholds. The experiment on the data set shows that all of our modules are effective and have great improvement as compared with other correlation filter type trackers.
Research on Cloud Classroom Construction of Higher Vocational Colleges Based on Campus Micro-Cloud Platform
Authors: Yin Lijing 
Published: January 2021

Abstract
With the rapid development and popularization of the Internet, the Internet era has arrived, and the Internet has entered people’s lives and has become an inseparable part. The system adopts RESTful architecture design and implements server-side interface to realize the unification of server interface; adopts JS (JavaScript) inter-modulation technology to realize it. The system adopts WeChat authorized login method to realize user login function. The system software is an online classroom software. Users need to be able to watch the course videos, so a player is required to play the course videos. After the experimental comparison and test, the improved server reduces the corresponding time and error rate of the system, and the instantaneous high concurrency performance of the network has been significantly improved, which can provide more stable technical support for the campus cloud classroom.
Research on Intelligent Image Recognition Technology Based on Equalization Algorithm
Authors: Qin Fang , Zeng Weijia , Ruijie Liu , Xuming Lu 
Published: January 2021

Abstract
In the image edge distortion correction, the straight-line projection-derived edge fit is poor, resulting in large correction errors. Therefore, an image edge distortion correction algorithm based on the equalization algorithm is proposed in the paper, where the adaptive threshold wavelet denoising algorithm is adopted to implement adaptive transformation of various scale parameters to complete noise removal. According to the results of image denoising, Fisher vector coding is used to optimize the results of the equalization algorithm to extract the edge distortion of the image. Besides, based on the edge distortion shape extraction results, the correction objective optimization function is obtained to analyze the edge fracture situation, realizing the linear projection derivative edge fitting. Meanwhile, by determining the image edge error evaluation function, the method of image edge distortion correction is judged to achieve the purpose of image edge distortion correction. Therefore, taking noisy barrel distortion and pincushion distortion images as the research object, the experimental analysis is carried out. The results show that the algorithm proposed in the paper can effectively correct the barrel distortion and pincushion distortion of the image, achieving high-precision and high-efficiency image edge distortion correction.
Innovative business model of energy Internet and construction of power grid enterprises
Authors: Wang Dawei , Wang Lixia , Li Wei 
Published: January 2021

Abstract
In this paper, the Internet and the power industry are deeply integrated to create a new development model, so as to realize the economic transformation from non clean energy driven to clean energy driven. Based on the theoretical framework of innovative business model, this paper adjusts the organizational structure, pays more attention to the cooperation with external institutions, so as to seek the market entrance of energy Internet, and studies the behavior of energy suppliers under the traditional business model and innovative business model from different angles.
The Electrical Properties of Single-walled Carbon Nanotubes
Authors: Liu Junzhi 
Published: January 2021

Abstract
Carbon nanotubes are expected to be a new type of semiconductor material to replace the traditional silicon-based semiconductor material. The high carrier mobility, ideal subthreshold characteristics and ballistic transport characteristics of carbon nanotubes can effectively solve the short channel effect, performance and power consumption limits of silicon-based semiconductor devices. These excellent properties are due to the structure of carbon nanotubes. This paper focuses on the relationship between the structure of carbon nanomaterials and their electrical properties, and the advantages and prospects of using carbon nanotubes as semiconductor materials are also described.
Application Analysis of Zinc Oxide Nanomaterials Based on Quantum Dots in Energy Environment
Authors: Xiao Yushi 
Published: January 2021

Abstract
Nano-zinc oxide is a new type of multifunctional inorganic material. Nano-ZnO has excellent photocatalytic performance, which can effectively convert harmful pollutants into inorganic substances such as H2O and CO2, and can effectively remove many refractory substances, thus having great application potential in pollution control. In this paper, ZnO quantum dots are selected as the research object, the nanostructures and various morphologies of ZnO quantum dots are introduced, and various methods for preparing ZnO nanomaterials and their applications in the field of energy and environment are summarized.
Designs of Q-switching Laser Power Supply with High Voltage Narrow Pulse
Authors: Zhou Ping , Ma Xiao , Shen Huaibo , Yang Xiaoyu , Zhao Chenxiao 
Published: January 2021

Abstract
Based on the pulse generation principle of Q-switching and cavity-dumping technologies, this paper designs a power supply with high voltage and narrow pulse for switching avalanche transistor with rising edge of ns level. In the power supply, an avalanche transistor and VOMS transistor are matched to design a driving circuit, which outputs 4.6ns narrow pulse signal. The circuit replaces the traditional cable transmission signal drive mode, reduces the whole signal transmission distance, and improves the signal transmission stability. It makes the two ends of the laser Q-switched crystal KD*P be pushed off instantaneously, which shortened the output laser pulse width from 18ns to 5.6ns. The power supply with high voltage and narrow pulse can be well used in many laser fields including communication, medical treatment, ocean exploration, etc.
A Complex Circuit Tolerance Analysis Method Based on Affine Analysis
Authors: Huo Xiangli , Ma Cong 
Published: January 2021

Abstract
In this paper, the selection of tolerance analysis objects and the uncertainty in the process of tolerance analysis for complex circuit systems are studied. A relatively perfect principle for determining the key circuits of tolerance analysis is formed. And considering the linear and nonlinear problems of circuits, based on the affine analysis method, a tolerance analysis method considering the influence of random factors is studied. Then the oretical basis and implement steps of the mfethod is given finally, and the application of the typical circuit in a product circuit system is verified.