bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of Database Programming Technology in Computer Software Engineering
Authors: Hong Yunguo 
Published: November 2020

Abstract
This article analyzes the application value of database programming technology. Its application value includes improving practical effects, improving information security, and reducing the probability of technical problems. Combining the application characteristics of database programming technology, this paper studies the selection of database types, database establishment, database storage mode selection, file encryption protection, programming technology optimization selection, software and hardware system update design. The purpose of this article is to improve the application effect of database programming technology and improve the design level of computer software engineering.
Multi-UAV Collaborative Search and Strike based on Reinforcement Learning
Authors: Liu Bo , Wang Xiaoping , Zhou Wen 
Published: November 2020

Abstract
The problem of multi-UAV collaboration is one of the important research contents of multi-UAV systems. Aiming at the optimal strategy of multi-UAV collaborative search and strike against learning targets for moving targets in an unknown environment, a multi-UAV collaborative search and strike algorithm based on multi-team reinforcement learning is proposed. First, we establish a search probability map for the environment to search for moving targets After finding the target, the moving target is allocated by auction to form an independent investigation team for different goals. Then each inspection team learns its optimal strategy in cycles at the same time. At the same time, by changing the learning speed and supervising the learning results during the learning process, the convergence and effectiveness of the learning results are guaranteed. The simulation results show that the UAV can better adapt to the dynamic environment through training. Under the condition of no prior information, it can effectively execute the search and strike task against the moving target with learning ability. And compared with the learning method of Markov decision process, the convergence speed and learning speed are faster.
Micro-cracks Detection of Polycrystalline Solar Cells with Transfer Learning
Authors: Zhang Nawei , Shan Shuo , Wei Haikun , Zhang Kanjian 
Published: November 2020

Abstract
As the photovoltaic (PV) systems are universally utilized in power systems, the defect of solar cells, the core components of PV system requires to be detected in a low-cost and high-efficiency method, because of its wide application and direct influence on power generation efficiency and system stability. The principal aim of this study is to apply transfer learning in defect detection of polycrystalline cells with image-level labelled mono images and unlabelled poly images. The experiment was conducted with DAN models based on a MK-MMD to represent the distance between source domain and target domain and Resnet-50 backbone to learn transferable features. Three contrast methods were designed to make comparison with DAN. It has been proved that DAN model based on MK-MMD and Resnet-50 shows a promising performance on transferring from monocrystalline tasks to polycrystalline tasks.
Research on Detecting Moving Objects in Football Match Video
Authors: Xu Hongyi , Lu Jialing , He Qun , Gu Lei , Feng Chi 
Published: November 2020

Abstract
In recent years, detecting moving objects in video has attracted extensive attention in the field of computer vision and pattern recognition. With football becoming one of the most popular sports, analyzing the process of football games is of commercial value and guiding significance. However, the previous research mainly focused on the event detection, and rarely on getting the trajectory of moving objects by directly extracting from the video. This article discussed the method of detecting moving objects in the football match video. We extract the marker lines and build the coordinate system based on LUV color space. And we use the 68 landmarks to recognize the human face and the Kalman filter approach to detect the football. Our research is aimed at helping the audience and football professionals in analyzing the player performance and team strategy.
Medical Image Segmentation Based on Combination Method
Authors: Liu Yi , Zhang Caiming 
Published: November 2020

Abstract
This paper studies the data segmentation of medical images based on compressed sensing. We do not use image data as interpolation data, but as constraints to construct surface patches with quadratic polynomial approximation accuracy, study the properties and correlation of pixels, and take these edge points as constraints, and map the discrete image data into continuous mathematical functions, so that the continuous function or point data not only has the shape recommended by the image data, but also has a higher segmentation accuracy, and then realize the fine segmentation of images, which provides a new research idea to solve the difficult problems of spatial data representation, and has good theoretical significance and application value.
Dual-Branch Feature Fusion Network for Single Image Super-Resolution
Authors: Wang Kai , Duanmu Chunjiang 
Published: November 2020

Abstract
Rcently, the research of single image super-resolution (SISR) based on deep learning has made great progress. However, most of the methods of super-resolution (SR) study use a simple chain structure to obtain higher super-resolution performance. Additionally, most methods do not fully utilize the hierarchical features generated in the middle of the network, thereby cannot achieve relatively high performance. In this paper, we propose a novel dual-branch feature fusion network (DBFFN) to address this two problems in image SR. The backbone of DBFFN is composed of multiple dual-branch feature fusion block (DBFFB) cascaded. The DBFFB has a dual-branch structure which mainly contains two parallel sub-networks, one extracts image fine features via densely connected convolution layers, the other one sub-network extracts image more contextual features by stacking dilated convolution layers. The features extracted from each hierarchical of the DBFFB are cascaded, and then feature fusion is performed. A skip connection is introduced to learn residual between input and output of the DBFFB. Especially, we use the deconvolution layer at the end of the network to enlarge the image. The results of the experiment are exciting, when the scale factor is 3, the performance of our network has 0.56dB, 0.6dB, and 0.79dB improvement on the set5, set14, and urban100 benchmark datasets compared with RDN.
A semantic segmentation method for exposed rebar on dam concrete based on Unet
Authors: Zhang Jun , Mo Jian , Xu Huajun , Liu Zixing 
Published: November 2020

Abstract
Exposed rebar is an essential factor affecting the safety of the dam. In the past, manual inspection is a significant way to monitor exposed rebar risk. However, it is time-consuming, inefficient and difficult to quantitative evaluate, such as the exposed rebar area. A semantic segmentation method based on the Unet is proposed to replace the manual inspection for the dam exposed rebar automatic detection. Thirty-eight high-resolution images of dam exposed rebar are collected. Unet and the VGG16 backbone are adopted. The results indicated that Unet’s mIoU on the test set reaches 0.94, which proves to be an efficient way to detect the dam exposed rebar.
Adoption of Ensemble Empirical Mode Decomposition Algorithm and Back Propagation Neural Network in Net Surface Solar Radiation Prediction
Authors: Wang Yiting , Tan Wenan , Gong Yide , Guo Kai , Tang Shan 
Published: November 2020

Abstract
To improve the prediction accuracy (PA) of net surface solar radiation (NSSR), a net surface solar radiation (NSSR) prediction model, named EEMD-BPNN, is proposed by adopting the ensemble empirical mode decomposition (EEMD) algorithm along with back propagation neural network (BPNN). In this paper, EEMD is used to extract the signals to reduce the influence of noise with physical significance from the time series of the original NSSR, so as to obtain the intrinsic mode functions and residual terms of different frequencies. As well as BPNN is used to establish a corresponding prediction model for each component of mode function. The proposed model has been applied and verified in test the daily total NSSR in Aksu region, Xinjiang. In the case study, the mean percentage error (MPE), mean bias error (MBE), root mean square error (RMSE), and correlation coefficient are taken as evaluation indexes, and the accuracy and applicability of the EEMD-BPNN for NSSR prediction are analyzed by comparing the prediction results of the EEMD-BPNN with the BPNN and H-S model. The results show that the predicted values (PVs) of EEMD-BPNN are closer to the actual data and have better correlation coefficient (R2=0.9615) compared with the prediction results of BPNN (R2=0.8703) and H-S model (R2=0.8373), and the error analysis indexes of the predicted results are all small. It indicates that the PA of EEMD-BPNN is improved obviously, which means the EEMD-BPNN has superiority in NSSR prediction and provides a new reference method for NSSR prediction.
Fruit Recognition Based on Convolution Neural Network
Authors: Zhan Yuhui , Chen Yuefen , Li Xiaokang , Chen Mengyao , Luo Zhaoqian 
Published: November 2020

Abstract
Traditional fruit recognition is mainly manual, which is not conducive to automation. Deep convolution neural network (DCNN) has a strong ability of feature learning and expression. It is helpful to realize intelligence in fruit sales market if it is applied to the identification of fruits. Due to the lack of standard image databases and various types of fruits, image data sets used in this paper are obtained through taking pictures of fruits and network download. Considering the small number of samples, in addition to using common image processing technology for data expansion, transfer learning technology based on vgg16 model is also adopted for fine tuning, which can reduce the training time and alleviate over fitting. Finally, six kinds of common fruits are chosen for experiments, and the test results show that the average recognition accuracy reaches 94.16%.
MMUU-Net: A Robust and Effective Network for Farmland Segmentation of Satellite Imagery
Authors: Gao Xumin , Liu Long , Gong Huaze 
Published: November 2020

Abstract
Aiming at the multi-scale characteristics of satellite imagery, and the adhesion phenomenon in the farmland segmentation results which is caused by the close distance between different farmland blocks, this paper proposes a robust and effective network based on U-Net for farmland segmentation of satellite imagery, which is called MMUU-Net. On the basis of adopting the encoder with higher classification accuracy network, adding ASPP (Atrous Spatial Pyramid Pooling) layer in the middle, and designing the multi-scale feature fusion module in the decoder, so that the multi-scale feature information is fully utilized; in order to better fuse multi-scale information, a more robust loss function is designed; finally, we propose a segmentation strategy of the coarse and refined two-stage to eliminate the adhesion phenomenon. Through the comparative experiments, it is verified that MMUU-Net is better than other segmentation networks, and can be effectively applied to the task of farmland segmentation of satellite imagery.
Adaptive data fusion method based on Kalman filter and its application in piston shape and position detection
Authors: Nan Leiguang , Ma Xinqiang , Ge Hailong , Ma Qingzeng , Li Yanlei , Cheng Wei 
Published: November 2020

Abstract
In order to meet the needs of production, this paper studies the detection data processing algorithm for the angle of the piston shape and position, the groove width and the runout of the groove bottom. An adaptive data fusion method based on Kalman filtering is proposed. This algorithm can use the adaptive method to calculate the different weight coefficients of each data point according to the standardized sampling data and the smoothed data points obtained by the cubic B-spline approximate fitting algorithm. This paper obtain the fusion data; then calculate the groove angle, groove width and groove bottom runout of the piston according to the conversion formula. Finally, experimental data verification is carried out, which shows the effectiveness and good robustness of the data processing of this algorithm. At the same time, a piston shape and position detection system was developed. The system rotates a circle at 90 degrees per second to obtain the measurement data. After the system software and hardware processing, the groove angle detection accuracy can reach ±2 minutes, the groove width and the groove bottom. The detection accuracy of the runout amount is within ±2 microns, and the measurement items in the measurement time and accuracy side meet the customer’s technical document requirements and achieve satisfactory results.
The nonlinear characteristics of transformer broadband
Authors: Huang Dongdong , Li Jiangtao , He Shuang , Zhu Yi 
Published: November 2020

Abstract
At present, the modeling of the transformer broadband nonlinear characters is based on the nonlinear features of 50Hz, so there are still some questions. In this paper, the magnetic hysteresis loop of the silicon steel in the the range of 50Hz to 100kHz is measured by using a measuring system composed of a 25cm Epstein square coil and an amh-1m magnetometer. The J-A parameters of the silicon steel core in the frequency wide range of the range of the optimized by using the hybrid means on the oneran fish swarm and L-M. Then, the nonlinear core model of transformer based on J-A parameters is set in PSPICE. Based on the model, the transformer simulation circuit is built. By applying sinusoidal voltage signal of the specific frequency to the nonlinear transformer model with the specific frequency, the non-linear characters of the transformer in wide frequency range areed. It is found the nonlinear characters of the transformer core change dramatically with the change of the frequency. Therefore, in the broadband nonlinear modeling of the transformer, using the nonlinear characters at 50Hz to nonlinear the broadband model will lead to large error. In the follow-up modeling research, scholars should consider the nonlinear characters of the transformer to the set the model, and try to use the nonlinear characters of the transformer under the frequencies for the broadbandlinear modeling.
Fault location method based on three-phase asymmetric phase current fault component and Euclidean distance
Authors: Liu Qingshi , Xu Huian , Li Yifei 
Published: November 2020

Abstract
When a single-phase ground fault occurs in the distribution network, the three-phase current fault components of the fault path and the non-fault path in the system will be difference which can be used as a criterion for fault line selection. However the three-phase load current in the system is much larger than the fault component current, the three-phase phase fault component current is not obvious, which will cause the failure of the method. This paper proposes an improved fault location method based on the three-phase phase current: the fault component of the three-phase current of the system is obtained by subtracting the phase current before the fault occurs from the after. And then the zero-sequence current of this line is subtracted from the fault component to enhances the difference between the lines, and the fundamental amplitude of the result is obtained. The algorithm uses the three-phase fundamental amplitude as the coordinate axis to establish a coordinate system. The difference between the fault path and the non-fault path is enhanced by calculating the Euclidean distance of the three-phase current fundamental amplitude. And the location of the fault section can be judged locally by comparing the overall differences between the lines. In this paper, the specific fault section location process and criteria are proposed, and the feasibility of the method is verified by simulation experiments and actual field data.
A Minimized Buffered Link Scheduling Algorithm for WirelessHART Networks Based on Graph Route
Authors: Luo Nanhang , Fang Mingjie , Zhao Kunming , Du Enwu 
Published: November 2020

Abstract
WirelessHART network is an emerging wireless sensor network technology, famous for simplicity, safety, and reliability. WirelessHART network uses Time Division Media Access (TDMA) and Frequency Hopping Spread Spectrum (FHSS) access mechanism to avoid collisions and mitigate interference from other wireless networks. Many formulations of the link scheduling problem have been proven to be NP-complete, which is about how to assign slots and channels to links. In this paper, we propose a minimized buffered link scheduling algorithm for WirelessHART networks based on graph route (LBLSGR), which utilizes edge coloring to scheduling time slots according to the resource demand of nodes. We supply more communication resource to the nodes located nearer to the gateway, because of frequent packet forwarding. Utilizing this algorithm, the WirelessHART networks can make the average network delay least, the average network power assumption lower, and the packet buffers minimized. At last, the validity of our algorithm is proved and the test result is provided.
Simulation Method of Load Characteristics of High-power PMSM Drive System
Authors: Lu En , Li Wei , Yang Xuefeng , Liu Yufei 
Published: November 2020

Abstract
This paper presents a simulation method of load characteristics of high-power permanent magnet synchronous motor (PMSM) drive system. According to the structural characteristics of the PMSM drive system of electric tractor, the load simulation experiment system is built to simulate the load characteristics of high-power PMSM. Next, on the basis of the similarity principle, the parametric equivalent scaling principle of the high-power PMSM drive system and experimental PMSM is established. Then, a dynamic load tracking algorithm is proposed based on the PI controller and the structure of load simulation experiment system, and the low-pass filter (LPF) is introduced to reduce noise interference caused by the differential term of speed. Finally, the numerical simulations and experiments are presented according to the schematic diagram of the load characteristic simulation and dynamic load tracking strategy. The results demonstrate that the proposed simulation method of load characteristics can effectively simulate the dynamic load of high-power PMSM drive system, which lays a foundation for the verification of the PMSM speed control strategy.
Research on AGV Scheduling System Based on Internet of Things
Authors: Wang Zhiyong , Ai Changsheng , Wang Zhe , Ren Gangchang , Xu Qiang 
Published: November 2020

Abstract
In this paper, in order to solve the needs of a pharmaceutical factory’s logistics system for multiple automated guided vehicles (AGV), a set of AGV scheduling system architecture was developed on the basis of the outdoor heavy-duty AGV that has been developed. Firstly, a WiFi wireless network communication environment is established in the factory area. The AGV side establishes a TCP/IP connection with the dispatching system through the MOXA bridge. The communication application layer uses the MQTT protocol widely used in the Internet of Things, and combines the production beats, considering the task distribution and path Planning and other possible realistic factors, developing scheduling software, and establishing a factory environment scheduling model. Through function test and performance test, the feasibility of the system is verified. This solution is conducive to improving the scheduling system’s ability to handle high concurrent tasks and increasing the cargo transportation throughput of the logistics system.
Simulation of Sensorless Control for FSPMLM with high-frequency signal injection
Authors: Meng Gaojun , Zhang Feng , Yuan Ye , Zhu Zhiying , Sun Yukun , Tao Xia 
Published: November 2020

Abstract
This paper probes into a sensorless control method of flux-switching permanent linear magnet (FSPMLM) with high-frequency signal injection, and analyses the FSPMLM structure through salient pole verification. Specifically, a high frequency sinusoidal voltage signal was superimposed into the estimated d-axis, and the mover position was demodulated after the regulation of q-axis current, which is relative to error angle. Ultimately, the exact mover position and speed were obtained. To solve the starting problem of the FSPMLM, improved magnetic pole identification was performed based on space vector pulse width modulation for an arbitrary initial position. Through the simulation of the FSPMLM and the control system, it is confirmed that the proposed approach is of high accuracy and effectiveness.
FPGA implementation of eight-direction Sobel edge detection algorithm based on adaptive threshold
Authors: Zhang Kaiqiang , Liao Qiang 
Published: November 2020

Abstract
As the basic content of digital image processing, edge detection is increasingly applied in machine vision, face recognition, target tracking and other fields. In particular, edge detection based on Sobel operator is generally applied in industrial practices. However, most current edge detections based on PC-side serial processing are extremely arduous to satisfy real-time processing. This paper proposes FPGA implementation of eight-direction Sobel edge detection algorithm based on adaptive threshold. Specifically, an eight-direction Sobel edge detection algorithm based on adaptive threshold is presented. Subsequently, the specific process of implementing the algorithm on FPGA using the design philosophy of parallel pipeline is described. Finally, a comparison of the performance between traditional Sobel and improved algorithm reveals that the improved algorithm offers distinct advantages in accuracy and detection effectiveness, and the parallel implementation of FPGA hardware can obviously enhance the detection speed and fulfil the real-time requirement.
Monitoring systems of the combustion processes based on optical spectral devices
Authors: Vaganov M A , Kazakov V I , Moskaletz O D 
Published: November 2020

Abstract
The article considers the application of optical spectroscopy methods to address the problems of physical and technological process control. Such processes often run under extreme conditions. Therefore, to eliminate the negative influence of these conditions on a spectral device, it is proposed to use an optical fibre transmission system. It is shown that these devices can be described as multidimensional systems, the characteristics of which are given in matrix form. This enables forming up to N informative parameters characterising the course of the process.