bluuebunny/crossref_metadata_2025 huggingface dataset:
Physics Edutainment Learning based on Engklek and Ontang-Anting Games: Creating Fun Physics for Students
Authors: Hariyono E , Rizki I A , Rizqillah I A , Citra N F , Shobah N , Zainuddin A
Published: December 2022

This study aims to describe students' responses to edutainment-based physics learning. The study was conducted with a qualitative approach involving 33 pre-service physics teachers. The data were analyzed using Miles and Huberman's model, including data reduction, data display, and conclusion drawing. The results showed that 52% of pre-serviceteachers did not know the existence of edutainment-based physics learning as a learning instrument to create exciting and fun physics learning. However, most pre-service physics teachers choose numbers from 7 to 10 to indicate their level of agreement for edutainment-based physics learning. This means that most pre-service physics teachers agree that traditional Engklek games and Ontang-anting are applied to learn physics based on edutainment physics.
Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network
Authors: Khoiro M , Firdaus R A , Suaebah E , Yantidewi M , Dzulkiflih
Published: December 2022

The effect of segmentation on lung X-ray image classification has been analyzed in this study. The 150 lung x-ray images in this study were separated into 78 as training data, 30 as validation data, and 42 as testing in three categories: normal lungs, effusion lungs, and cancer lungs. In pre-processing, the images were modified by adaptive histogram equalization to improve image quality and increase image contrast. The segmentation aims to mark the image by contouring the lung area obtained from the thresholding and some morphological manipulation processes such as filling holes, area openings, and labelling. Image classification uses Convolutional Neural Network (CNN) with five convolution layers, an Adam optimizer, and 30 epochs. The segmentation effect is analyzed by comparing the classification performance of the segmented and unsegmented images. In the study, the unsegmented X-ray image dataset classification reached an overall accuracy of 59.52% in the network testing process. The segmented X-ray image dataset obtained greater accuracy, 73.81%. It indicated that the segmentation process could improve network performance because the input pattern of the segmented image is easier to classify. Furthermore, the segmentation technique in the study can be one of the alternatives to developing image classification technologies, especially for medical image diagnosis. Segmentation Effect on Lungs X-Ray Image Classification Using Convolution Neural Network.
Pascal or Archimedes: Which Misconception is Higher?
Authors: Irawati R K , Sofianto E W N , Assidiqi H , Rahmawati I
Published: December 2022

The purpose of this research was to know the higher misconception between Pascal's law and Archimedes' law. The research method was qualitative descriptive. The question analysis is used to know the misconception using the four-tier test. The research subject was senior high school students in South Borneo with the purposive sampling technique. The object of this research was the senior high school students who had received the fluid static material. The result of the research showed that Archimedes' Law Material was the material with higher misconception than Pascal's law. The misconception of Archimedes' law had 51.62%, and Pascal's law at 20.52%. The implication of this research was to determine suitable learning on the Archimedes Law learning.
Correlation study between chaos features of physiological signals and emotions based on the information gain method
Authors: Nie Chunyan , Wang Huiyu
Published: December 2022

Clarifying the correlation between the chaos features of physiological signals and emotions plays an important role in improving the accuracy of emotion recognition. In this paper, the correlations of chaos features such as Lyapunov exponent, complexity, box dimension, and approximate entropy of physiological signals with positive, negative, and neutral emotions are analyzed and studied using the information gain method. Based on this, the Bagging-CHAID model is used for emotion recognition validation analysis. The results show that (1) the approximate entropy of physiological signals has the greatest correlation with positive, negative, and neutral emotions, followed by complexity and Lyapunov exponent features, and box dimensional features have the least correlation with the three emotions, thus, the approximate entropy can be chosen to improve the emotion validated based on Bagging-CHAID model, which showed the accuracy of the correlation analysis.
Study on Process Parameters of Low-power Semiconductor Laser Stripping PTFE Insulating Layer of Aviation Wire
Authors: Li Pin , Huang Huiping , Wang Ping , Huang Guangwen , Zhou Peimin , Tian Chunlin
Published: December 2022

Faced with the increasingly high-quality requirements for the insulation layer stripping at the end of aviation wires, laser wire stripping technology is becoming more and more popular due to its good processing quality and production efficiency. Traditional laser wire strippers generally have problems of bulky size and high control cost and are not suitable for the decentralized processing of large wire harness products. This work explores the feasibility of using a low-power semiconductor laser to strip the PTFE insulating layer of aviation wires. The influence of the parameter changes of each processing factor on the cut morphology of the insulating layer was studied separately. It was found that higher laser power removed more insulating layers per unit of time, but reduced the cutting quality. High laser scanning speed can obtain good insulation cutting quality, but too large or too small scanning speed will reduce the stripping efficiency. Simultaneous improvements in insulation cutting quality and efficiency depend on the right mix of power and scanning speed.
Development of Special Geomagnetic Data Acquisition System with Low Cost
Authors: Sun Hong-zhi , Zhao Lei , Yang Zhen-peng , Ren Xue , Luo Fei , Sun Yi-mei , Wang Qi , Gong Fei
Published: December 2022

It is known to all that the wide monitoring of geomagnetic anomalies requires geomagnetic detection equipment to be of low cost, low power, and high stability. On the basis of the weak magnetic signal detection technology, a weak magnetic signal acquisition system with low-cost magnetic data acquisition, data processing, and data analysis is established by using a three-dimensional SEN-R65 magnetic sensor, magnetic signal acquisition chip PNI 11096, and P89C668 chips. Firstly, the hardware of a low-cost geomagnetic data acquisition system is designed, and the corresponding data processing and analysis programs are written. Based on the designed hardware circuit and data processing for functions such as acquisition, transmission, storage, and real-time display of geomagnetic signals, it is found that the developed instrument meets the expectations of the low-cost geomagnetic data acquisition system designed in this paper, indicating that the system is applicable.
A Novel Interpolation Method for Cutting Trajectories in Spatial Coherent Cutting Machine
Authors: Wang Dewu , Zhang Cheng , Xi Xin
Published: December 2022

Coherent lines are generally more complex spatial curves, and the study of coherent lines is the basis for studying cutting trajectories of spatial coherent cutting machines. In this paper, the spatial geometry problem is transformed into an algebraic problem solving by establishing a mathematical model of spatial coherent line to find its parametric equations. Interpolation refers to the process of data densification. In this paper, a speed planning method that adapts to the curvature change of the Non-Uniform Rational B-Splines ( NURBS ) curve is proposed. After inputting the starting points and endpoints to the Computer Numerical Control (CNC), the computer will calculate the coordinate data of each step according to the characteristics of the line segment, carry out the speed planning and then complete the curve trajectory interpolation. The accuracy of the algorithm is verified by comparing the theoretical coherent trajectory and interpolated trajectory.
Missing Detection of The Clutch-Driven Disc Spring Assembly
Authors: Hou Daishuang , Gao Jingang
Published: December 2022

A detection system for the missing assembly of clutch-driven plate springs is established. The algorithms used in this system, such as point cloud data preprocessing, spring area positioning, and spring missing detection, are studied. Firstly, according to the characteristics of the clutch-driven plate spring point cloud, the Gaussian statistical method is used to eliminate the outliers incurred by system error and other reasons, and the two-sided filtering is performed on the clutch-driven plate spring point cloud that eliminates the outliers to eliminate the noise points with small ups and downs. Then, the theoretical division area is constructed by using the theoretical width between the clutch-driven plate springs and the theoretical width of the springs. Compared with actually recorded positions, the theoretical area is expanded so that the real spring point cloud data are in the corresponding positioning area; finally, the spring in the region is identified by using the characteristics of an inconsistent number of point clouds in different assembly conditions, so as to detect the missing assembly of the clutch-driven plate spring. The experimental results show that the detection accuracy of the clutch-driven plate spring assembly is as high as 100%. It can basically meet the detection requirements for the missing assembly of clutch-driven plate springs.
Research on Mobile Terminal Mobility Prediction Method Based on Unscented Kalman Filter
Authors: Zhang Jing
Published: December 2022

For mobile terminal users in the process of moving, due to the uncertainty of movement, how to effectively predict and reasonably allocate the Edge Servers (ESs) for mobile terminal users has become the key to solving the problem. Therefore, this paper proposes and designs a mobile terminal user mobility prediction method based on an Unscented Kalman Filter (UKF). According to the application scenario of Mobile Edge Computing (MEC), the edge server with computing resources is placed at the edge node of the network. The state equation and observation equation are constructed through the trajectory points of the mobile terminal users, and the trajectory prediction is completed using the UKF. By comparing the No Filter. Method, the Linear Kalman Filter. Method and the UKF method, the research results show that the proposed method can effectively reduce the error and improve the success rate of the task compared with the other two methods so that the prediction trajectory of mobile terminal users is more accurate and more applicable.
Vibration Analysis of the Non-abrasive Cryogenic Polishing Experimental Devices
Authors: Xu Anjun , Wang Yan , Qi Zhihui , Mu Guozhen , Lv Qiongying
Published: December 2022

Non-abrasive cryogenic polishing is a new method that can obtain ultra-smooth surfaces. For experimental devices, the vibration displacement, vibration frequency, and natural frequency can be obtained through model analysis under a 3D experimental model using ANSYS. Results show that the faster the ice wheel rotates, the smaller vibration of the experimental devices, and the higher the vibration frequency.
Grinding and Polishing Repair Technology and Experimental Research on Surface Defects of Flywheel Disc Semi-Finished Products
Authors: Shi Guangfeng , Mao Yuhang , Che Jianwei , Guan Yanji
Published: December 2022

The flywheel disc is an important engine component that is widely used in industries such as automobiles, ships, and flywheel energy storage. In this paper, single-factor experiment, and orthogonal experiment are used to explore the influence of abrasive particle size, grinding speed, grinding pressure, and grinding time on the surface roughness and material removal rate of flywheel disc semi-finished products (cold-rolled steel). The results show that the optimal combination of parameters for the surface defect repair process of flywheel disc semi-finished products is: abrasive particle size of W20, grinding speed of 40 r/min, grinding pressure of 3 psi, grinding time of 40 min, and an average surface roughness of 342nm. The repaired flywheel disc semi-finished product has great surface quality and meets the requirements of factory repair.
Research on Control Strategy of Buck Converter Based on Particle Swarm Optimization Fuzzy PID
Authors: Zou Jincheng , Xue Peng
Published: December 2022

Because buck converter is a typical non-minimum phase system, the traditional PID algorithm has the disadvantages of low control accuracy and high system overshoot, while fuzzy PID control only slightly improves the control effect. Therefore, this paper designs a control strategy based on particle swarm optimization fuzzy PID algorithm to enhance the control effect of the buck converter and combines chaotic variables to optimize the quality of the initial particle swarm. Finally, Matlab simulation experiments verify that the control strategy of fuzzy PID optimization using particle swarm optimization combined with chaotic variables has the advantages of short response time, high stability, and low system overshoot compared with the traditional PID algorithm and fuzzy PID algorithm.
Parameter Optimization of PID Controller Based on Improved Beetle Herd Algorithm
Authors: Li Jun , Chen Xingwu
Published: December 2022

The PID controller, although used in 95% of the loop operations in the automation field for practical control, still has a lot of shortcomings such as nonlinearity, hysteresis, and time-varying uncertainty, resulting in unsatisfactory control effects. To solve this problem, this paper proposes an improved beetle herd algorithm for PID parameter optimization. With the combination of the group collaborative optimization ability of particle swarm optimization, the global search and local search capability is strengthened through the test of the error-index ITAE and the optimized PID parameters. Finally, with the help of the PID controller simulation, the effectiveness of the algorithm is verified by comparing the effects of the four algorithms before and after the improvement.
Research Progress of Robot Vision Sensing Weld Seam Tracking Technology
Authors: Hu Zhiwei , Cheng Wei , Ge Hailong , Li Wenlong , Lu Tong
Published: December 2022

As the key technology of intelligent welding, seam tracking plays a vital role in the welding process. The development and application of visual sensing technology has laid the foundation for obtaining accurate weld position information. The intelligent combination of visual sensing and welding robots improves the stability and accuracy of welding seam tracking technology. This paper introduces the composition and principle of the welding seam tracking system, and the classification of visual sensing technology. This paper summarizes and analyses the welding seam image processing method and the research status of robot tracking control technology around the process of welding seam tracking. This paper summarizes the current problems of weld tracking technology and looks forward to the future development trend.
Signal generation and regulation of implantable nerve stimulator
Authors: Zhang Qinglei
Published: December 2022

In the treatment of nervous system diseases, implantable nerve stimulators have attracted more and more attention because of their obvious therapeutic efficacy and high safety. The generation and regulation of stimulus signals are the most important step in the design of an implantable nerve stimulator. The stimulation signals from the implanted nerve stimulator act directly on nerve cells. If the stimulus signal is not delivered accurately according to the set parameters, it will not only affect the therapeutic effect but also cause unpredictable malignant consequences. To generate stimulus signals that meet certain requirements, the different circuit structures need to be designed to be applied to the stimulators with different function types. In this paper, five types of stimulator circuits are introduced, and their strengths and weaknesses are analyzed based on the detailed introduction of the working principles of the different stimulator circuits, which might provide a theoretical and practical reference for future research.
Finger rehabilitation training and evaluation system based on EEG signals, machine learning and Fugl-Meyer scale
Authors: Liang Xiaohu , Zhao Qicheng , Liang Junming
Published: December 2022

For patients with digital hemiplegia caused by stroke, equipment such as mechanical exoskeleton devices are currently used to help recovery, but they are limited to many shortcomings of this method. This paper proposes to collect electroencephalogram(EEG) information through brain-computer interface (BCI) equipment. Combined with the research status of BCI system at home and abroad, we established the research idea of BCI system based on motor imagery by preprocessing the obtained information. According to the biological characteristics of human fingers and the needs of finger rehabilitation, a finger rehabilitation system was designed to assist hemiplegic patients in rehabilitation training. In the experiment, the EEG signals of several subjects in the two states of imagining finger movement and rest were collected through the EEG cap, and an appropriate feature extraction method was selected. Machine learning like logistic regression, random forest and deep learning were used for various classifications. The EEG feature vectors extracted by different subjects were classified and cross-validated. At the same time, the popular Fugl-Meyer scale was selected to quantitatively assess the patient’s limb function. The experimental results show that the comprehensive classification effect of deep learning is the best. The system performed well for the patient’s recovery process.
Mobile Robot Trajectory Estimation Algorithm Based on Adaptive EKF
Authors: Yi Chengqun , Wu Jiaye , Li Jiali
Published: December 2022

An adaptive control system based on extended Kalman filtering is designed for internal and external interference in real-world track-tracking tasks. First, the performance state of the wheeled robot at work is analyzed, and a kinetic model is developed. Then, the extended Kalman filter is used to achieve the real-time prediction of the external environmental disturbance amount, so as to obtain the error information that needs to be improved, and its error is eliminated. Finally, Lyapunov analysis is used to derive the stability of the improved rear wheel robot system and then confirm the feasibility of the scheme. The simulation verification analysis shows that the trajectory of the wheeled robot can be stably tracked steadily, which proves the feasibility and effectiveness of the proposed algorithm.
Research on Distribution Network Topology Identification Algorithm Based on Mutual Information Bayesian Network
Authors: Xia Zaiqi , Du Jun , Jiang Fan , Guo Yanpeng , Song Shaohua
Published: December 2022

In this paper, a distribution network topology identification algorithm based on mutual information Bayesian network is proposed. In this algorithm, the Bayesian model is mainly used to fit the nonlinear relationship among various variables such as photovoltaic, load, measured voltage, and distribution network topology. Then, the mutual information network is used to effectively divide the measurement continuous stage intervals of variables, thus improving the identification and analysis accuracy of the distribution network topology by Bayesian network, and solving the problems existing in traditional Bayesian network applications, such as difficult continuous data processing, poor adaptability of many continuous deformations, etc.
A Deployment Method for Unmanned Aerial Vehicles Based on Improved Neural Network
Authors: Yang Wenda , Wu Minggong , Wen Xiangxi , Zhang Zhe
Published: December 2022

The time for drones to complete their missions is limited. When sensors detect targets or perform multiple tasks, the pressure of insufficient resources can be quite high, and even task overload may lead to task failure. This paper focuses on a multi-UAV target assignment method. Compared with the classical model-solving method, which can find the satisfactory solution in a short time. Finally, through MATLAB simulation and verification, we find that the method in this paper has better dynamic adaptability to the complex and changeable environments.
Development and application of the multifunctional transmission roll-sliding friction tester
Authors: Shao Jiayin , Yi Lili , Han Wenhe
Published: December 2022

This paper aims to elaborate on the development and the main features of the multifunctional transmission roll-sliding friction tester. The tester adopts the independent drive for controlled relative movement, and the contacting specimen that sustains the radial loads can be replaced, so it can complete the rolling friction, sliding friction, and roll-sliding friction test. The integrated transmission performance test machine not only has solved the inconsistency between the friction and transmission experimental boundary conditions but also can precede the friction drive research. Piecewise calibration data and interpolating correction algorithm are used to avoid the affecting generated by the spindle friction to the specimen. The monitoring system software is developed by the LabVIEW platform, and the parameters are controlled automatically by the computer. Thus it can simulate actual working conditions very well and achieve automatic real-time data acquisition, processing and showing the results, etc.
Analysis and Experimental Research on Dynamic Characteristics of Horizontal Shaft System of Photoelectric Equipment
Authors: Liu Lei , Jiang WeiWei , He Bi
Published: December 2022

In order to study the transmission characteristics of vibration in the shafting of optoelectronic equipment, a horizontal shafting test platform for optoelectronic equipment is designed. Firstly, according to the special structure and load conditions of the three-row ball-column combined turntable bearing used in the horizontal shafting, a quasi-static model of the bearing load was established to solve the load distribution of the bearing and further calculate the bearing stiffness. At the same time, the bearing system was established. The dynamic model of the system is used to calculate the frequency response function of the system under the conditions of radial load and axial load and analyze the vibration transmission characteristics of the shaft system. Based on the finite element method, a finite element calculation model is established, the shafting dynamic model established is used to simplify the bearing, the modal parameters of the equipment are analyzed, and the experimental verification is carried out using the Siemens dynamic test system. The results show that the established model is effective and reliable. It provides a reference for studying the dynamic characteristics of the shaft system of optoelectronic equipment.
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

Aiming 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
An Improved Weighted Differential Evolution Algorithm Based on the Chaotic Mapping and Dynamic Reverse Learning Strategy
Authors: Wu Fengbin , Li Shaobo , Zhang Junxing , Lv Dongchao , Wu Xuefang , Li Menghan
Published: December 2022

The weighted differential evolution algorithm has the disadvantage of slipping into local optima and low convergence. To address these problems, an extended weighted differential evolution algorithm based on the chaotic mapping and optimal-worst dynamic opposite learning strategy is proposed. Firstly, the chaotic sequence generated by improved one-dimensional Logistic-Chebyshev mapping is employed to modify the starting population generated uniformly within the original algorithm, which aids in broadening the richness of the population. Secondly, to augment the probability of obtaining global optimum, an optimal-worst dynamic reverse learning strategy is used. Finally, a new evolutionary step size generation method is incorporated into the algorithm, which helps to acquire better solutions. The experimental outcomes obtained by testing 10 benchmark functions in CEC 2020 indicate that the proposed method has been greatly improved in accuracy.