bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on the influence of Welding Head using the process on Welding Performance of Spot Welding Machine
Authors: Jin Zhao , Qiu Yafeng , Yan Hao , Xu Dingbang , Hou Xiaoming 
Published: December 2022

AbstractAiming at alloys commonly used in terminals, this paper studies the method of improving the characterization performance of the spot welding section from the perspective of controlling spot welding process parameters. Using the control experiment, by controlling the welding current, energization time, electrode pressure, and using coolant during welding, the surface structure image and the maximum tensile-shear force curve were obtained, so as to obtain the optimal spot welding process parameters. Then use ANSYS finite element analysis software to simulate the resistance spot welding process, and use the model of the macro morphology, microstructure, hardness distribution, and shear resistance of the spot welding head to verify the optimal welding process parameters of resistance spot welding.
Effect of hollow core on cooling temperature in 3D printing
Authors: Wei Xiaoyu , Wan Yi , Liang Xichang 
Published: December 2022

Abstract
3D printing is a very promising method for sand mold manufacturing with a shorter design time and simpler manufacturing process compared with conventional technology in the casting industry. The heat preservation and feeding effect of the riser can effectively reduce the occurrence of shrinkage cavity and porosity in the casting. Therefore, the insulation effect of the riser is an important index to evaluate sand mold design. In this study, a hollow sand core was designed and used for casting a cylindrical thin-walled part by the binder jetting 3D printing method. The temperature change curve of the hollow core during the No. 35 steel pouring process and the riser site during the A356 aluminum alloy solidification process in this sand mold was tested to explore the effect of the hollow core on the cooling process. The A356 pouring results showed that the concretion time of the riser with a hollow core was prolonged by over 20%; the cooling rate of casting was accelerated by over 20% by applying this hollow core before sand shakeout. The No. 35 steel pouring facts reflected that the temperature rise rate of the solid core was more visible than that of the hollow core in the early process of casting solidification. The work in this paper indicated that a sand core with a hollow structure could not only accelerate casting cooling but also draw out the concretion duration of the riser to improve its supplying function and can reduce the thermal expansion of the sand core. This study provides a new and effective design idea for 3D printed sand core, which can be used as a reference for the design of 3D sand core molds and the research of casting solidification and cooling.
Research on motor speed control algorithm of UAV Based on Reinforcement Learning
Authors: Li Zhongxing , He Jiufeng , Ma Haixia , Huang Guojian , Li Junyu 
Published: December 2022

Abstract
Aiming at the hovering control problem of four-rotor UAVs, this paper proposes to use the reinforcement learning method to control the motor speed of UAVs to improve the intellectual control level of four-rotor UAVs. Firstly, two reinforcement learning algorithm models of DDPG (deep deterministic policy gradient) and PPO (proximal policy optimization) are built using the PARL framework, and the super parameters of the algorithm model are set. Secondly, the algorithms are trained and optimized to obtain scores in the RLSchool simulation environment. Finally, the performance differences between the two algorithm models in the same simulation environment are analyzed. The test and analysis results show that the two algorithms can realize the four-axis aircraft suspension control in the simulation environment. Between them, the PPO algorithm features high scores, simple and convenient parameter adjustment, stable control, and good performance.
Research on Kinematics Analysis and Trajectory Planning of UR5 Robot
Authors: Wen Zhexin , Liu Feifei , Dou Xiangfei , Chen Jie , Zhou Dong 
Published: December 2022

Abstract
In order to solve the problems in the practical application of the UR5 robot, improve the work efficiency and ensure the stability of the movement, the related kinematics analysis and trajectory planning of the UR5 robot is carried out, and the D-H parameter kinematics model is established to carry out forward and reverse motion. The transformation relationship of each link coordinate system in the forward kinematics and the rotation angle of each joint in the inverse kinematics are solved, and then MATLAB is used to perform related calculations to verify the feasibility of the algorithm. The robot arm performs interpolation to complete the trajectory planning between the beginning and end points passing through multiple intermediate nodes.
Design of Smart Control System of Wing Anti-icing
Authors: Liu Chi , Xue Zhandong , Zhang Xueling 
Published: December 2022

Abstract
A self-adaptive neural network-based control method in aircraft anti-ice systems is introduced. To get better accuracy and stability in wing anti-ice system control, a self-adaptive PID control is applied to drive the heater groups distributed around the wing and powered by remote power distribution units. Unlike the traditional system, the type of heater command is substituted from constant frequency to vary the frequency, and the control signals are given by PID controller whose three parameters propagation, integral and derivative, are given by backpropagation neural networks. Furthermore, in the tuning process, the plant’s predictive output is used to modify the weights of neural networks. The plant’s output will also be predicted by BP neural networks, and it is a nonlinear prediction that improves the predictive accuracy. The implementation and simulation program was written by MATLAB/Simulink.
DCGAN-based optimal scheduling for energy internet
Authors: Cui Yong , Gu Jun , Cao Liang , Chai Wei , Zhang Jianxin , Zhan Gui 
Published: December 2022

Abstract
With the gradual depletion of fossil energy sources, the energy internet is attracting more and more attention. However, the energy internet faces problems such as source-load uncertainty and data deficiency in operation, which bring challenges to the reliability and stability of energy internet scheduling operations. Therefore, this paper proposes a DCGAN-based optimal scheduling method for the energy internet. First, DCGAN is used to generate energy internet operation scenarios to provide data support for optimal scheduling of the energy internet. Then, a multi-energy coupled optimal scheduling model of the energy internet is constructed, and optimal scheduling is performed for the generated scenarios. The experimental results show that the proposed method can realize the optimal scheduling of energy internet for the energy internet uncertainty problem and data deficiency problem.
Calculation and Analysis of Heat Transfer Characteristics of Internal Threaded Tube at High Reynolds Number
Authors: Zhang Xiaohua , Yu Jie , Zheng Songsong , Gao Chunrui 
Published: December 2022

Abstract
In the actual operation of a heat storage steam boiler heat exchanger, an internal threaded tube is used as a heat exchange element. Usually, the heat exchanger volume is large and there are many consumables because of the complex composition of flue gas in the pipe so the internal threaded tube in the actual long operation process will suffer from ash accumulation and serious wear phenomenon. In this paper, the heat transfer process of internally threaded tubes is taken as the main research object, and the convection-enhanced heat transfer of internally threaded tubes at a high Reynolds number is deeply studied. For gas under the condition of high Reynolds number, the strengthening of turbulent flow inside of inner grooved tubes convective heat transfer process of deduction and analysis is derived. A fluid calculation model which is more in line with the heat transfer mechanism of threaded pipe is proposed, which provides a reference for the subsequent theoretical studies of heat transfer enhancement of threaded pipe.
A high-resolution upwind numerical scheme for solving transient multiphase flow in wellbore
Authors: Sun Dalin , Sun Baojiang , Wang Zhiyuan , Lou Wenqiang 
Published: December 2022

Abstract
The identification of gas-liquid interface is the basis for accurate calculation of wellbore flow parameters after gas invasion, which plays an important role in the safety and reliability of drilling engineering. At present, most petroleum engineering uses a simple difference algorithm to calculate the wellbore multiphase flow, this scheme can not capture the gas-liquid interface accurately, and the numerical accuracy is low. Therefore, this paper proposes a mathematical and numerical calculation model for transient multiphase flow in wellbore during gas invasion. Using second-order Runge-Kutta method and OMUSCL2 method, a high-precision and high-resolution upwind AUSMV numerical scheme is constructed to capture the gas-liquid interface. The research shows that the high-precision and high-resolution upwind AUSMV scheme can accurately capture the gas-liquid interface, and thus accurately solve the multiphase flow parameters in wellbore during the gas invasion.
An integrated anti-interference suppression strategy based on L-type and π type circuit series for integrated distribution box
Authors: Liu Meijie , Cao Zhen , Hu Weihua , Wang Zhuo 
Published: December 2022

Abstract
With the wide application of intelligent distribution equipment, the operation and management level of the distribution network has been significantly improved. At present, domestic and foreign scholars' researches on the primary and secondary integration technology of distribution switch are still in its infancy. And it needs to be further discussed. Based on the structure theory of the distribution terminal acquisition unit, this paper analyzes the influence of temperature rise, pressure, and strong electromagnetic interference on the performance of primary and secondary fusion equipment under field operating conditions. To improve the operation reliability of the integrated equipment, an anti-interference suppression measure of the primary and secondary fusion distribution switch is proposed based on the series connection of L-type and π-type circuits and the accuracy detection method of the transformer. It solves the problem of high-frequency damping oscillation effectively at the input of the transformer under the condition of electromagnetic interference. This paper verifies the feasibility and effectiveness of the integrated fusion technology of the primary and secondary fusion equipment of distribution switches through theory and experiment.
A High Concurrency and Low-Delay Data Scheduling Method for Massive Power Devices
Authors: Ouyang Benkai , Xian Zhao , Yin Zhenchao , Chen Yao , Chen Xiangyuan 
Published: December 2022

Abstract
With the massive processing devices connected to the power system, the demand for data processing increases gradually. However, in the high concurrency scenario, the simultaneous processing of massive data will lead to the depletion of computing resources of the power system and the suspension of power services. To address this problem, this paper first proposes the classification method of power system data based on a neural network. Secondly, this paper proposes the data priority model based on the entropy method to determine the processing order of massive data of power devices. Then, a problem of high concurrency and low-delay data scheduling is constructed. To address this problem, we proposed an intelligent data scheduling algorithm based on upper confidence bound (UCB). Finally, the feasibility and effectiveness of the method are proved by simulation.
Drug-target interaction prediction using high order nonlinear features via neural factorization machines
Authors: Qian Quan , Zhu Yuan 
Published: December 2022

Abstract
Medication design and repositioning are sped up by the prediction of drug-target interactions (DTIs). Two main kinds of prediction methods are commonly used, which are based on chemical structure feature extraction and deep learning methods. However, on the one hand, the DTI prediction approaches based on chemical structural feature extraction may not wholly explore the possible network characteristics in the data. On the other hand, many deep learning methods call for numerous layers of neural networks to be layered to learn higher-order feature interaction information. To sum up, the existing computation methods often have the limitations of gradient disappearance and overfitting. This study presents a novel method (JRD-NFM) by calculating Jaccard similarities, getting an eigenvector through Restarted random walk (RWR), and generating low-dimensional feature vectors by Disposition Component Analysis (DCA). Besides decoding the topological features and similarity information of target and drug node, it can also get the context information of a single network. Considering the advantages of Neural Factorization Machines (NFM) in extracting high-order nonlinear features and processing sparse data, this study use NFM to classifier the collection data to integrate drug and target biochemical structure information. The experimental results demonstrate that JRD-NFM can outperform widely used deep learning methods and conventional chemical structure approaches. It could provide fresh ideas for combining molecular structure and heterogeneous network data to predict DTIs.
Research on Obstacle Avoidance and Positioning Technology of UAVs in Substation
Authors: Yi Liu , Guo Qingbin , Chen Jiawen , Liu Xiaowei , Li Jiahao 
Published: December 2022

Abstract
UAV obstacle avoidance technology is directly related to work safety in substations, and is an important topic in the current domestic power system. Therefore, the obstacle avoidance and positioning technology of UAVs in substations are deeply studied. Through camera imaging, matching image features are extracted. And camera imaging can be combined with ultrasonic obstacle avoidance to realize the overall obstacle avoidance technology of UAVs. The coordinates are established through parameter optimization, and the substation UAV positioning technology is realized after the positioning accuracy is clarified. This solution can effectively solve the contradiction between the workload of the substation system and human and material resources, detect risks in time, ensure safety, provide a complete and reliable guarantee for the unattended mode in the substation, and achieve the autonomy of UAVs in the substation and positioning and obstacle avoidance functions.

CLC number: TM744 Document identification code: A.
Typhoon Identification and Positioning Using the FY-4A AGRI Thermal Infrared Data
Authors: He Ming-Chuan , Jiang Geng-Ming 
Published: December 2022

Abstract
This paper addresses typhoon identification and positioning by using the thermal infrared data acquired by the Advanced Geostationary Radiation Imager (AGRI) on the Chinese Fengyun 4A (FY-4A) satellite. First, a training dataset, a validation dataset, and a test dataset of typhoons in the West Pacific Ocean close to China are created from the FY-4A AGRI thermal infrared data. Then, the YOLOX neural network is configured and trained, in which an average precision (AP) of 33.2 is obtained for the test dataset. Finally, with the prior knowledge that the brightness temperature of the typhoon eye is higher than that of its surroundings, the typhoon eyes are located using the morphological image processing method. The results of typhoon eye positioning are generally consistent with the Optimal Path (OP) dataset of tropical cyclones created by the China Meteorology Administration (CMA), and the mean errors in latitude and longitude are 0.0391° and 0.0334°, respectively.
Research on Medical Information Extraction and Recognition of Lung Tumor Based on Convolutional Neural Network
Authors: Zhao Tianyu , Dai Hang 
Published: December 2022

Abstract
With the improvement of the social economy, lung cancer has gradually become one of the main reasons for endangering human security. The tumor is benign or malignant, which can reduce the cumbersome examination in the early stage, prevent unnecessary surgery, reduce the psychological and physical pain of patients, and improve the survival rate of patients. As a common method of treatment and detection of treatment response in the medical field, the medical image has become the main technical means of cancer diagnosis and treatment in the clinic. This paper studies the extraction and recognition technology of lung tumor medical information based on a convolutional neural network (CNN), and the method used is CNN. CNN provides an end-to-end learning model. The parameters in the model can be trained by the traditional gradient descent method. The trained CNN can learn the features in the image, and complete the extraction and classification of image features. After research, the algorithm in this paper is effective and suitable for wide use.
Formation Control of Multiple Mobile Robots Based on Fuzzy Adaptive Sliding Mode Control
Authors: Zhang Hao , Gong Yulin , Liu Jianhua 
Published: December 2022

Abstract
In order to solve the problems of slow convergence and chattering in formation control of multiple mobile robots, and to optimize the dynamic characteristics of formation system, a hybrid adaptive controller is proposed by analyzing the characteristics of different sliding mode reaching laws, which can accelerate the convergence speed and effectively suppress the chattering of the system. Firstly, the saturation function is introduced into the double power reaching law to form the saturation double power reaching law; Secondly, the hyperbolic tangent reaching law is used to improve the stability of the system when it approaches the sliding surface; Finally, fuzzy logic rules are used to adjust the coefficients of saturated double-power reaching law to optimize the overall dynamic parameters. Simulation results show that the designed controller can improve the convergence speed of the system, effectively suppress the chattering, and realize the fast and stable following of the follower robot to the pilot robot, which verifies the correctness and availability of the proposed controller.
An underwater image enhancement method based on color correction and local unsharp masking
Authors: Yang Yicun , Yun Haijiao 
Published: December 2022

Abstract
The underwater images collected by optical cameras have different problems due to the camera equipment, underwater environment and light source, which lead to the problems of chromatic aberration and low contrast of the images collected by ordinary people for subsequent use. The reasons are that the reflection of light by impurities in the water and the absorption efficiency of different wavelengths of light under water, and under different depths of field conditions often leads to color Bias and contrast are low. To solve this problem, we adopt a method based on color correction and local unsharp masking for the fusion of underwater images. Firstly, color is improved by correcting for color shift using the red channel and gray world methods, combined with CLAHE. secondly, for the low contrast problem, the adaptive mask coefficient and the local unsharp mask are used to separate the background and foreground, which enhances the recognition of underwater target and background, and effectively improves the contrast of images. Target. Finally, the weighted fusion of Gaussian pyramid is completed by using the method of combining four weights. The experimental results show that the algorithm proposed in this paper has a strong generalization ability, and can improve the quality of images in different underwater scenes which is according to different objective evaluation indicators, the algorithm in this paper also achieves the best results.
Prediction of Surface Subsidence Based on PSO-BP Neural Network
Authors: Cai Hui , Wang Yulin , Song Chuanwang , Wang Tongtong , Shen Yuan 
Published: December 2022

Abstract
The traditional settlement monitoring has the problems of small monitoring range and low adaptability of settlement prediction model; Based on SBAS-InSAR technology and PSO-BP neural network algorithm, this paper constructs a PSO-BP settlement prediction model based on Sentinel-1A data. Take Jinan(E116°8’, N36°8’)the regional monitoring data is a data sample set. The algorithm proposed in this paper is compared with typical algorithms such as SVM. The results show that the MAE of 30 points predicted by this algorithm is 0.17, which is better than six typical algorithms such as SVM. This algorithm can be used as an effective means to provide early warning of surface subsidence.
3D PathPlanning of RobotFishBased on ImprovedAntColonyAlgorithm
Authors: Wu Mengyu , Zhang Yonglin , Liu Ni , Dai Lei 
Published: December 2022

Abstract
For the following problems:three-dimensional obstacle avoidance path selection when using robot fish to find targets in a complex coral reef geographical environment, an improved ant colony algorithm that can be applied in an environment with multiple complexities is proposed. Combined with the underwater terrain, several maps with different obstacle complexities are established with the three-dimensional environment modeling scheme. Aiming at easy stagnation, slow search speed, and easiness to produce local optimization in the conventional ant colony algorithm, this paper improved the function design and helped the robot fish carry out the early search based on three pieces of heuristic information (the global distance, traffic ability cost, and path consumption cost). Then the path is sorted according to the path length to adjust the pheromone update mode. At the same time, an elite ant system is constructed to adjust the pheromone value of elite ants separately. The simulation results show that convergence speed and searchabilityof the algorithm have been improved to some extent, which proves feasibility of the algorithm.
Predicting Misalignments of the Material Test System through a Thick-flat Specimen by Machine Learning
Authors: Chen Junxian , Zhang Jianhai 
Published: December 2022

Abstract
Material properties are often determined by conducting uniaxial tests on a variety of materials. The precision of the material testing system is critical to the reliability of the mechanical properties of the tested materials. Among the significant factors affecting the performance of the material testing system is its loading coaxiality. Since predicting misalignments involves high border nonlinearity, it is challenging to quantify misalignments. To assess the alignment deviations of a tensile test system., on a thick-flat specimen, a feed-forward neural network was employed for the first time to develop a mapped relation involving misalignments and strain distribution. Additionally, we utilized the genetic algorithm to optimize the essential hyperparameters (learning rate, batch size, and the nodes in the hidden layer) of the created deep feed-forward network. Simulation results indicated that the optimized neural network could reach an accuracy of 99.89% in the prediction of misalignments. Predicted misalignments can measure the test errors associated with the material testing system to avoid incorrect conclusions from being drawn because of systematic errors. The proposed method could coordinate the location of the uniaxial test equipment during manufacturing and assembly.