bluuebunny/crossref_metadata_2025_split huggingface dataset:
Software defect prediction based on CS-BP neural network
Authors: Ma Jiawang 
Published: September 2021

Abstract
Software defect prediction can detect whether there are defects in the program module so as to effectively reduce the unnecessary cost of software development and maintenance. In this paper, the limitation of the traditional BP neural network in the field of defect prediction leads to the inaccuracy of the prediction results. By using the global optimization ability of cuckoo search, the BP neural network is improved, the important initial parameters of the network are optimized, and the software defect prediction method of CS-BP is proposed. The experimental results show that compared with traditional machine learning algorithms such as BP neural network, J48 and SVM, CS-BP method has a better effect on the prediction of software defects.
Massive MIMO Channel Estimation Based on Compressed Sensing
Authors: Bai Xiaojuan , Guan Lu , Zhao Chao , Dao Wei 
Published: September 2021

Abstract
Obtaining accurate channel state information (CSI) is an important prerequisite for achieving good performance of the Massive MIMO system. The traditional pilot-based channel estimation algorithm has low spectrum utilization. The channel estimation algorithm based on compressed sensing can make full use of the sparsity of the channel and realize channel estimation with fewer pilots. In this paper, wavelet denoising is combined with a sparse channel estimation algorithm based on Orthogonal Matching Pursuit (OMP), and Cramer-Rao Lower Bound (CRLB) is derived. The simulation results show that this method has better performance.
Numerical Investigation of Aeroelastic Deformation Effect on the NASA Common Research Model
Authors: Zhang Lu , Xiao Zhongyun , Ma Shuai , Liu Fan 
Published: September 2021

Abstract
The risk of the aeroelastic deformation of the wings to the flight safety has become increasingly prominent for commercial transport aircraft due to the requirements for lightweight structure. In this work, Reynolds averaged Navier-Stokes (RANS) method coupled with transfinite interpolation were employed to investigate the influence of static elastic deformation of wing on aerodynamic characteristics of NASA CRM. Both bend and twist distributions were scaled in numerical simulations based on dynamic pressure, which was shown to be reasonable by the compassions between the calculations and measurements. The numerical results show premature flow separation in the middle and outboard of the wing for original configuration. Furthermore, the flow separation was supressed by the upward bending of the wing and negative twist in the leading edge, and the aerodynamic characteristics of the aircraft were improved by the aeroelastic deformation.
Network security prediction method based on kubernetes
Authors: Ruan Zhengping 
Published: September 2021

Abstract
In order to improve the stability and security of network operation, a network security prediction method based on kubernetes is proposed. CPU resources are managed by kubernetes. Based on this, terminal data and resources are converged, and network extension is standardized according to the final situation value. Combined with POS algorithm, starting from the group theory, through the cooperation and competition between different particles in the network, a particle swarm optimization model of “network running speed network communication” is established. Through the relationship of particle swarm optimization in the network, the SVR parameters are optimized. The network security is predicted by extracting the characteristics of network load. Through the application of the design method, it is proved that this method can realize the effective prediction of network operation security.
Simulation and Research of DME Signal Coverage
Authors: Zhang Xianhao , Zhuang Gangqiang , Guo Kang , Luo Zhongmei 
Published: September 2021

Abstract
In civil aviation land-based navigation equipment, the DME(Distance Measuring Equipment) system is a widely used short-range navigation equipment. Through the research on the antenna of the DME equipment, the antenna signal of the DME equipment is modeled and simulated, and the electromagnetic signal three-dimensional pattern of the DME equipment and the corresponding pattern of the E plane are obtained. On the basis of mastering the antenna signal radiation characteristics of each navigation device, the DME coverage area is simulated and analyzed.
Ultra-short-term Wind Power Output Prediction Based On LS-WMC Combined Model
Authors: Du Yanan , Pang Zhengning , Qi Jingxian 
Published: September 2021

Abstract
In this paper, an ultra short term wind power output prediction method based on least square and weighted Markov chain model is proposed. Firstly, the wind farm output is fitted based on the least square method, and then the prediction accuracy is improved by using the weighted Markov chain model. At the same time, the partial exponential weighting method is used to improve the prediction accuracy of ultra short-term wind power output on the basis of reducing the generation frequency of transition probability matrix, so as to optimize and improve the prediction results. The results show that the method can improve the prediction accuracy.
Latent Fine-grained Features embedding model for Unsupervised Zero-shot Learning
Authors: Tian Guiyu , Tao YiZheng , Xie Yang 
Published: September 2021

Abstract
Zero-shot learning (ZSL) aims to identify image categories that never appear in the training set by learning a mapping from visual information to the semantic space. For existing works in recent years, it has been paying much attention to user-defined-attributes-based supervised ZSL models and manual annotation-based unsupervised ZSL methods, whilst the important drawback of user-defined attributes is ignored. User-defined attributes usually need to be precisely annotated not only for seen classes but also for unseen classes. This procedure of collecting user-defined attributes is an error-prone and time-consuming work limiting the scalability of the methods to a great extent. Moreover, user-defined attributes are semantic embedding but they are not exhaustive. In this paper, we propose a manual annotation-free unsupervised ZSL method with a great scalability which is a benefit for the large-scale ZSL tasks, just using public word vectors of categories without any dedicated attributes annotation efforts. In addition, our approach can automatically extract latent fine-grained features to reduce visual information losses caused by the absence of user-defined attributes. Extensive experimental results show that our method outperforms the previous methods among the manual annotation-free unsupervised ZSL methods.
Research on phase diagram of traffic flow in a single loop roundabout under open boundary condition
Authors: Xue Wandong , Zhang XiaoDan , Jia JinFang , Zhao Yu 
Published: September 2021

Abstract
The cellular automation traffic flow model of roundabout with inner-roundabout-lane, injection-roundabout-lane and extraction-roundabout-lane is used to study the traffic flow of single loop roundabout under open boundary conditions. Through computer simulation, the relationships between the entrance probability, exit probability, vehicle braking probability and system flow, the average speed of each lane and system in the open boundary conditions are studied.Besides, the space-time diagram of vehicles on the injection-roundabout-lane with different entrance exit probabilities and the system phase diagram are established. In the system phase diagram, the critical entry probability and exit probability values of each part of the phase diagram under different braking probability are given. The results show that the system traffic flow can be divided into three phases: the phase in which the system flow changes with the entrance probability, the phase in which the system flow changes with the exit probability, and the phase in which the system flow has nothing to do with the entrance probability and exit probability. This will provide some guidance for road management.
Li-ion battery state of health Prediction based on Long Short-Term Memory Recurrent Neural Network
Authors: Lin Jingdong , Yan Guansong , Wang Chang 
Published: September 2021

Abstract
The state of health (SOH) prediction of lithium-ion battery is essential for the health management of batteries. At present, the prediction method combined with the extraction of health indicators in charge-discharge process has received extensive attention, however, many studies ignored that the extraction of battery discharge data will be affected by the actual operating conditions, which will affect the effectiveness of health indicators extraction. In this work, a type of recurrent neural network (RNN), which is long short-term memory-RNN(LSTM-RNN), is proposed to prediction the SOH of Li-ion batteries through the data of charging process and capacity. Because the different choice of network parameters will also affect the performance of the model, particle swarm optimization (PSO) algorithm is used to optimize LSTM model. The test results show that this method can effectively predict SOH of battery, and the maximum RMSE is less than 0.01. Compared with the traditional LSTM algorithm, it has higher accuracy.
Image Compressed Sensing and Reconstruction of Multi-Scale Residual Network Combined with Channel Attention Mechanism
Authors: Zeng Chunyan , Wang Zhenghui , Wang Zhifeng , Yan Kang , Yu Yan 
Published: September 2021

Abstract
Most of the existing compressed sensing reconstruction algorithms based on deep learning only use simple stacked convolutional layers to extract image feature information, which cannot extract sufficient image information; and each feature maps are treated equally in the reconstruction process, which lacks the ability of discriminative learning across feature channels and can’t make full use of the representation capabilities of convolutional neural network. This paper proposes a new multi-scale image compressed sensing reconstruction network based on residual network and channel attention mechanism. We use the residual network to increase the network depth, which reduces the information loss in the convolutional process through the skip connections in the residual block. It can obtain richer image information and protect the integrity of data information. We also add channel attention mechanism to adaptively scale each feature maps to enhance effective feature information and suppress invalid feature information. The experimental results show that the performance of image reconstruction is greatly ameliorated compared with the existing the state-of-the-art methods.
Inverse dynamics modeling of robots based on sparse spectral gaussian process regression
Authors: Huang Cuiyi , Wang Yang 
Published: September 2021

Abstract
In robot control systems, inverse dynamics calculation of the robotic arm is essential. In this paper, we propose a different inverse dynamics modeling method—sparse spectral Gaussian process regression (SSGPR). Since using Gaussian process regression for robot inverse dynamics modeling is computationally intensive, to address this drawback, it is proposed to apply sparse spectral Gaussian process regression on robot inverse dynamics modeling, the core of which is the sparsification of the GP’s spectrum for the purpose of improving computational efficiency. Experiments show that sparse spectral Gaussian process regression can improve the computational efficiency and outperform other regression methods while ensuring the computational accuracy.
An image processing toolbox based on MATLAB GUI
Authors: Deng Qianyu , Chen Xu 
Published: September 2021

Abstract
Our research realizes the basic processing of images based on MATLAB GUI. The functions include image reading, storage, display, adding noise, denoising, image enhancement, edge detection, image segmentation, discrete Fourier transform, wavelet transform, feature extraction, target recognition, color image processing, etc. At the same time, the image can be stylized and different filter processing can be carried out on the image.
Research on Facial Expression Recognition Based on Improved Deep Residual Network Model
Authors: Chen Ji , Luo Xiaoshu , Meng Zhiming , Li Wanting 
Published: September 2021

Abstract
Facial expressions are the main external manifestations of human emotions. Facial expression recognition technology can be used in medical, investigation, education and other application scenarios. Aiming at the disadvantages of slow convergence speed and low recognition accuracy of traditional neural networks, in order to optimize the network more efficiently and improve the recognition rate of facial expressions, a facial expression recognition method based on improved convolutional neural networks is proposed.First, the deep residual network ResNet18 is improved, and then the improved ResNet18 network is used to extract global expression features of face images, and then the self-attention weighting module is introduced to calculate the expression features of each face image and output one The corresponding weights are used to weight the loss function. Finally, the two public expression data sets of CK+ and RAF-DB are used to experimentally verify the method in the article. The accuracy of facial expression recognition reaches 98.89% and respectively. 87.13%, compared with the original deep residual network ResNet18 model and other types of network models, the facial expression recognition rate has been significantly improved, which proves that this method can effectively improve the accuracy of facial expression recognition and has certain application value.
Land classification based on high resolution remote sensing images
Authors: Su Ziyang , Zhong Yuanchang 
Published: September 2021

Abstract
Land information is an important information related to the timely adjustment of land management policy, which can significantly improve the efficiency of land management. The traditional land information collection completely depends on field survey of grass-roots personnel, which is not only time-consuming and laborious but also can not guarantee the accuracy of information. With the rapid development of remote sensing technology, using remote sensing image to obtain land information is an important means to conduct land investigation and detect whether crop planting area is up to the standard. At present, the method of artificial neural network (ANN) learning classification has achieved great success in the land classification, but there is still room for improvement in the classification accuracy. In this paper, a new Algorithm is introduced-shuffled frog leaping algorithm (SFLA), the important parameters of ANN are optimized. The traditional traversal method is improved to a combination of global optimization and local optimization, which can effectively reduce the search time, increase the generalization ability of the model and improve the classification accuracy. Through the experiment of remote sensing data of Fengqi town, Luochuan county, Yan’an city, Shaanxi province, it is shown that SFLA can effectively increase the accuracy of ANN in land information classification. It has achieved the paper research goal.
Research on Ultrasonic Localization Method of Moving Objects Based on Improved TOA Algorithm
Authors: Zhang Xinghong , Chen Ran , Xu Yi , Sun Bingman , Zeng Fanfeng 
Published: September 2021

Abstract
In the process of ultrasonic positioning of moving objects, a position algorithm TOA is optimized and improved due to the problem of delay caused by sequential ranging of the sequential ranging of the ultrasonic transmitter and receiver, the system’s solving time, and the signal transmission time. By setting an error compensation factor for the velocity of the object to be located and the unit positioning time of the system, the error is compensated to achieve the same positioning accuracy when the object to be located is stationary. The simulation results show that the positioning accuracy can be stabilized within 3 cm at a certain speed, and the positioning can be performed well for objects to be positioned with a certain speed.
A passive DDoS attack detection approach based on abnormal analysis in SDN environment
Authors: Sun Shimin , Zhang Xinchao , Huang Wentian , Xu Aixin , Wang Xiaofan , Han Li 
Published: September 2021

Abstract
Most of the detection methods against DDoS attacks are based on periodic detection, which leads to high communication overhead, untimely detection, and slow attack response. This paper proposes a passive abnormality detection approach. First, we record the two flow-table characteristics of the regular switches. Then, based on dynamic threshold method and Grubbs outlier test method, we make a determination of abnormal switches. This method reduces the amount of data duplication and regular traffic collection. Moreover, we use Support Vector Machine (SVM) algorithm to evaluate the performance of the passive anomaly detection method. The experiment results show a better performance than active period DDoS attack detection approaches.
Color speckle measurement in laser display
Authors: Shu Wei , Liu Hong 
Published: September 2021

AbstractIn recent years, with the continuous development of laser projection display technology, people’s choice of electronic display equipment has become more diversified.However, due to the high coherence of the laser used for imaging in the laser projection display device, the speckle phenomenon is more obvious than other display devices, which will greatly affect the user’s actual perception. A color speckle measurement device based on XYZ filter is designed. The computer software is used to control the camera to take speckle pictures under different filters, and calculate the speckle contrast of red, green, blue and white fields. A color speckle measurement device based on XYZ filter is designed. The computer software is used to control the camera to take speckle pictures under different filters, and calculate the speckle contrast of red, green, blue and white fields.
Improved YOLOv4 Power Insulator Fault Detection
Authors: Zhang Zhen , Kong Shuaihua , Peng Kun 
Published: September 2021

Abstract
In order to achieve accurate real-time monitoring of power insulators on transmission lines, an improved YOLOv4 network model is proposed to detect power insulator faults. Experimental results show that the mAP value of the improved YOLOv4 network model reaches 93.01%, and the detection frame rate reaches 44 frames/s. Compared with the YOLOv4 and Faster R-CNN models, it can be concluded that the proposed model can better meet the needs of power insulator fault detection for transmission lines.
A fault diagnosis equipment of motor bearing based on sound signal and CNN
Authors: Li Dong , Li Binbin , Wang Chaoqun , Cheng Pengyu , Jiao Bin 
Published: September 2021

Abstract
As an important part of mechanical equipment, the motor bearing damage rate is very high. In order to realize the fast and accurate diagnosis of motor bearing faults, this paper designs a fault diagnosis equipment based on sound signals. First, perform wavelet transform on the collected sound signal, then use the spectrogram generated by the fast Fourier transform to preliminarily determine whether the motor bearing is faulty, and finally use the convolutional neural network model that has been imported into the processor to diagnose the faulty parts of the motor bearing, The accuracy rate is above 98.41%.
Research on Web Robot Detection Technology for Concept Drift
Authors: Chen Xue , Song Yang , Xiong Wei , Lu Yutao , Wang Xingen 
Published: September 2021

Abstract
Web robots have long been significant participants of the internet and the detection is deemed to be essential. Contemporary web robot detection techniques lack the ability to detect when the characteristics of web robots drastically change over time, namely a phenomenon called concept drift. Such change of web robot’s behavior may lead to the deterioration of detection model performance. In order to maintain high detection performance over time, in this paper, we propose a novel web robot detection framework that consists of a model pool, a reinforcement learning algorithm integrating all the models and a concept drift detection module. First, we employ reinforcement learning to integrate a number of detection models that targets different types of web robots, by dynamically adjusting model weights. Then we apply the drift detection method (DDM) to monitor concept drift and identify the need to retrain the model over time. Experiments are conducted using real website datasets under concept drift. The results demonstrate that our model significantly outperforms the state-of-the-art approaches.
Modeling of multilevel control system based on Event-B
Authors: Wang Yutian 
Published: September 2021

Abstract
Aiming at the problems existing in the traditional development method of multilevel control system, this paper proposes a formal modeling solution based on Event-B from the perspective of practical application. Based on the analysis of four synchronization modes and the principle of multilevel control system, a formal method is introduced. On this basis, by formulating the refined strategy of the multilevel control system model, the iUML-B model meeting the security requirements is established step by step. Through the automatic transformation function of the Rodin, the iUML-B model of multilevel control system is automatically transformed into Event-B mode. In order to ensure the safety of the system, this paper uses the ProB to simulate and record the event trajectory. The results show that the formal modeling method based on Event-B greatly improves the security and development efficiency of industrial control.
Research and Application of Substation Safety Communication Based on MMS Alternative Protocol
Authors: Luo Huafeng , Shen Yifei , Xu Ai , Lu Chengyu , Ruan Lixiang , Du Qi-wei 
Published: September 2021

Abstract
At present, most substations are constructed based on IEC 61850 standard. The communication protocols in the station mainly include MMS, GOOSE and SMV, which are gradually exposed in engineering practice. Some deficiencies and potential safety hazards have emerged. For this reason, based on the MMS alternative protocol, the function and security of its implementation are introduced. In terms of functions, MMS alternative protocol adds multiple services and optimizes the deficiencies of the existing MMS protocol; for security, the application layer of the MMS alternative protocol implements two-way identity authentication based on the SM2 algorithm and despatching certificate, preventing unauthorized devices to prevent illegal access and attacks from the source of protocol; the transport layer of the MMS alternative protocol implements full link encryption and authentication based on the SM2 algorithm and despatching certificate to ensure the transmitted data message is antiforgery and non-repudiation and antitheft, protect the confidentiality and integrity of data messages. Therefore, the MMS alternative protocol has been greatly optimized on function, and the security authentication and transmission encryption mechanism of the protocol are focused on the security aspect. The final experimental results prove that the MMS alternative protocol has a certain reference significance in engineering applications.