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2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC)最新文献

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Research on friction disturbance compensation method in low-speed region of permanent magnet synchronous motor 永磁同步电机低速区摩擦干扰补偿方法研究
2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) Pub Date : 2022-03-04 DOI: 10.1109/ITOEC53115.2022.9734508
Yuchen Wang, Zhonghua Wang
{"title":"Research on friction disturbance compensation method in low-speed region of permanent magnet synchronous motor","authors":"Yuchen Wang, Zhonghua Wang","doi":"10.1109/ITOEC53115.2022.9734508","DOIUrl":"https://doi.org/10.1109/ITOEC53115.2022.9734508","url":null,"abstract":"In this paper, the instability phenomenon of permanent magnet synchronous motor (PMSM) running in a low-speed region is compensated. Based on the PI control strategy, the friction model is used to compensate for the nonlinear part dominated by friction torque. The Stribeck friction model is used to build the model of system friction. Compensation of linear load disturbance using load observer. The simulation results show that the friction model and the disturbance observer method reduce the influence of friction nonlinearity on the low-speed performance of the system and improve the tracking accuracy and disturbance rejection ability of the system.","PeriodicalId":127300,"journal":{"name":"2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123417715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Research on non-contact measurement based on machine vision 基于机器视觉的非接触式测量研究
2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) Pub Date : 2022-03-04 DOI: 10.1109/ITOEC53115.2022.9734442
Yuhao Zha, Yanpei Luo, Yao Ding, Tao Yu
{"title":"Research on non-contact measurement based on machine vision","authors":"Yuhao Zha, Yanpei Luo, Yao Ding, Tao Yu","doi":"10.1109/ITOEC53115.2022.9734442","DOIUrl":"https://doi.org/10.1109/ITOEC53115.2022.9734442","url":null,"abstract":"In industrial measurement, due to the limitation of working environment, it is often impossible to carry out manual measurement. Using geometric optics and image processing technology, the target can be measured indirectly through affine calculation. This paper takes the 7mm standard gauge block of national standard level 1 as the target to be measured, compares the measurement results under the environment of front and rear light sources, extracts the edge corners of the target to be measured by sub-pixel corner detection, and proposes a method to measure the target width based on iterative calculation of point line distance. For 7mm target, the calculation result is 7.052mm and the error is 0.74%, which is better than the measurement accuracy based on the minimum circumscribed rectangle.","PeriodicalId":127300,"journal":{"name":"2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC)","volume":"627 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116469392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Construction of Knowledge Graph of Power Communication Planning based on Deep Learning 基于深度学习的电力通信规划知识图谱构建
2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) Pub Date : 2022-03-04 DOI: 10.1109/ITOEC53115.2022.9734544
Sun Haibo, Li Sunxin, Tong Weiyue, Li Li
{"title":"Construction of Knowledge Graph of Power Communication Planning based on Deep Learning","authors":"Sun Haibo, Li Sunxin, Tong Weiyue, Li Li","doi":"10.1109/ITOEC53115.2022.9734544","DOIUrl":"https://doi.org/10.1109/ITOEC53115.2022.9734544","url":null,"abstract":"In order to improve the intelligent level of power communication network planning, a method for constructing a knowledge graph of power communication planning based on deep learning is proposed for the problems of lengthy power communication planning report text and low information extraction efficiency. This article takes the power communication planning text as the research object, constructs the knowledge organization structure of the power communication knowledge graph from top to bottom, and defines the entity concept and the relation concept. A variety of deep learning models are comprehensively used for knowledge extraction. Bi-LSTM-CRF model is used for named entity recognition, and PCNN model is used for entity relationship extraction, forming entity relationship table in power communication planning text. The effectiveness of the above method is verified by simulation experiment. Finally, the data storage and visualization are realized through Neo4j graph database.","PeriodicalId":127300,"journal":{"name":"2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121651649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Gaussian Process for the Machine Learning-based Smart fire Detection System 基于机器学习的智能火灾探测系统的高斯过程
2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) Pub Date : 2022-03-04 DOI: 10.1109/ITOEC53115.2022.9734697
Xinyuan Wan, Jianbin Cai, Shengxiang Luo, Zhengxing Tian, Li Zhang, Xiaojian Xia
{"title":"Gaussian Process for the Machine Learning-based Smart fire Detection System","authors":"Xinyuan Wan, Jianbin Cai, Shengxiang Luo, Zhengxing Tian, Li Zhang, Xiaojian Xia","doi":"10.1109/ITOEC53115.2022.9734697","DOIUrl":"https://doi.org/10.1109/ITOEC53115.2022.9734697","url":null,"abstract":"Smart fire detection systems should be able to detect the fire and trigger the automatic alarm at an early stage. It should also trigger the automatic fire extinguishing system and broadcast the fire alarm under different fire conditions. Due to the strict detection accuracy requirement of the fire detection system, most of the modern smart fire detection systems are based on multi-sensor, or image/video surveillance system to reinforce its fast reaction and high reliability in the action process. In this paper, the multi-sensor detection system is combined with image recognition process. Image recognition is utilized to help the fire detection, when the decision from the multi-sensor system is uncertain or the data is not available/faulty. Image features are extracted by using machine learning methods. Then, the Gaussian classification method is applied to detect the specific fire case. Images from real environments are used to evaluate the proposed method. In addition, we investigate and discuss the detection results when the training data is adequate or inadequate, which verifies that the image-based fire detection scheme combined with multi-sensor system can achieve better accuracy.","PeriodicalId":127300,"journal":{"name":"2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC)","volume":"91 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113943909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Algorithm for Recognition of Foreign Objects in Transmission Lines with Small Samples 一种小样本输电线路中异物识别算法
2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) Pub Date : 2022-03-04 DOI: 10.1109/ITOEC53115.2022.9734647
Xiaojing Liu, Xianda Chen, Shuai Cao, Jianlei Gou, Haozhi Wang
{"title":"An Algorithm for Recognition of Foreign Objects in Transmission Lines with Small Samples","authors":"Xiaojing Liu, Xianda Chen, Shuai Cao, Jianlei Gou, Haozhi Wang","doi":"10.1109/ITOEC53115.2022.9734647","DOIUrl":"https://doi.org/10.1109/ITOEC53115.2022.9734647","url":null,"abstract":"Accurate identification of foreign body images in transmission line channels requires a large number of samples for model training, but the actual foreign body image data sets that can be used for model training are seriously insufficient. In order to solve the problems of model failure, over-fitting and low accuracy caused by too few training samples, a new method for image recognition of foreign objects in transmission line channels under small sample conditions is proposed. This method enhances the image Technology and meta-learning technology are combined to train the U-Net image segmentation network, and finally obtain the foreign body image recognition model of the transmission line channel. Experiments were carried out on the foreign body recognition models that use meta-learning method and those that do not use meta-learning. The results show that the proposed method can accurately identify foreign body images of transmission line channels under a small-scale original data set, and the accuracy rate is greatly improved.","PeriodicalId":127300,"journal":{"name":"2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114726510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Swing Speed Control Strategy of Fuzzy PID Roadheader Based on PSO-BP Algorithm 基于PSO-BP算法的模糊PID掘进机摆速控制策略
2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) Pub Date : 2022-03-04 DOI: 10.1109/ITOEC53115.2022.9734429
Feng Li, Yongjie Li, Changqing Yan, Chenyin Ma, Chi Liu, Qihan Suo
{"title":"Swing Speed Control Strategy of Fuzzy PID Roadheader Based on PSO-BP Algorithm","authors":"Feng Li, Yongjie Li, Changqing Yan, Chenyin Ma, Chi Liu, Qihan Suo","doi":"10.1109/ITOEC53115.2022.9734429","DOIUrl":"https://doi.org/10.1109/ITOEC53115.2022.9734429","url":null,"abstract":"Aiming at the problem that the boom-type roadheader cannot quickly adjust the cutting swing speed to adapt to the hardness of the coal and rock when the coal hardness changes in coal mines, a control strategy for the driving swing speed is proposed. In this strategy, firstly, the PSO-BP neural network is used to construct a cutting load recognizer to provide a basis for adjusting the cutting swing speed of the roadheader; secondly, the PID control is optimized based on the fuzzy algorithm, and the fuzzy PID controller is established to improve the regulation of the cutting The efficiency of the swing speed; Finally, the roadheader swing speed simulation control system model is built in Matlab/Simulink, and the proposed roadheader cutting swing speed control strategy is simulated. The simulation experiment results show that the roadheader swing speed adjustment system using PSO neural network algorithm combined with fuzzy PID control has significantly improved response speed and control accuracy, and has good superiority and stability. The strategy based on particle swarm BP neural network algorithm combined with fuzzy PID control can provide certain theoretical guidance for stabilizing the cutting motor power of the roadheader and improving the efficiency of roadway work.","PeriodicalId":127300,"journal":{"name":"2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127553978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
GhostSiamRPN++: Optimizing SiamRPN++ via GhostModule ghostsiamrp++:通过GhostModule优化siamrp++
2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) Pub Date : 2022-03-04 DOI: 10.1109/ITOEC53115.2022.9734677
Da Li, Wensheng Tao, Yabing Kang, Xing Xiang
{"title":"GhostSiamRPN++: Optimizing SiamRPN++ via GhostModule","authors":"Da Li, Wensheng Tao, Yabing Kang, Xing Xiang","doi":"10.1109/ITOEC53115.2022.9734677","DOIUrl":"https://doi.org/10.1109/ITOEC53115.2022.9734677","url":null,"abstract":"SiamRPN++ is a Siamese network based tracker in object tracking and it has excellent performance. However, SiamRPN++ is a ResNet-driven tracker with huge amounts of parameters and calculations. It has extremely strict requirements for hardware and devices without professional GPU are difficult to meet the requirements. So SiamRPN++ should become more lightweight by optimization. In this paper, we propose a lightweight tracker named GhostSiamRPN++ by optimizing SiamRPN++ and replacing the ResNet backbone with a lightweight backbone build by GhostModule. When compared to SiamRPN++, GhostSiamRPN++ reduce 84% parameters, 92% calculations and 52% memory usage and it boost the FPS by 55% on GPU and by 545% on CPU. Large scale optimization does not bring great loss of performance. When tested on VOT and OTB datasets, GhostSiamRPN++ shows excellent performance in all videos and leading performance in some specific videos and it has 4.5% to 13.6% lower accuracy than SiamRPN++.","PeriodicalId":127300,"journal":{"name":"2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126364007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Prediction of foF2 frequency based on BP neural network with single point extrapolation 基于单点外推BP神经网络的foF2频率预测
2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) Pub Date : 2022-03-04 DOI: 10.1109/ITOEC53115.2022.9734535
Zhen Fang, Jianjun Shen, Xuequan Zhou
{"title":"Prediction of foF2 frequency based on BP neural network with single point extrapolation","authors":"Zhen Fang, Jianjun Shen, Xuequan Zhou","doi":"10.1109/ITOEC53115.2022.9734535","DOIUrl":"https://doi.org/10.1109/ITOEC53115.2022.9734535","url":null,"abstract":"In this paper, a BP neural network prediction model based on single point extrapolation is used to improve the prediction accuracy of ionospheric foF2. According to the optimization of parameters of input layer analysis to build appropriate training samples, by trial and error method to determine the hidden layer structure, using the experiment and simulation to train network to forecast, adopt the method of average test analysis prediction error, the final analysis of single point extrapolation and prediction of IRI2016 prediction model of BP neural network.","PeriodicalId":127300,"journal":{"name":"2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127986980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on Startup of Synchronous Reluctance Motor Based on Square Wave Injection 基于方波注入的同步磁阻电机启动研究
2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) Pub Date : 2022-03-04 DOI: 10.1109/ITOEC53115.2022.9734665
Dengke Li
{"title":"Research on Startup of Synchronous Reluctance Motor Based on Square Wave Injection","authors":"Dengke Li","doi":"10.1109/ITOEC53115.2022.9734665","DOIUrl":"https://doi.org/10.1109/ITOEC53115.2022.9734665","url":null,"abstract":"For the position sensorless vector control of synchronous reluctance motor, the starting performance affects the control effect at medium and high speed. In order to improve the overall control effect, it is necessary to improve the starting performance. This paper studies and improves the starting effect of synchronous reluctance motor. Taking advantage of the salient pole characteristics of the synchronous reluctance motor rotor structure, the method used in this paper is the high frequency square wave injection method. By injecting a high frequency square wave voltage signal, the rotor position information is obtained from the high frequency response current. The method can accurately estimate the rotor position at zero and low speed, and realize the reliable starting of the synchronous reluctance motor. The simulation results show that the method improves the startup effect of the synchronous reluctance motor and verifies the correctness of the control algorithm.","PeriodicalId":127300,"journal":{"name":"2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115912161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Multi-Agent Vehicle Formation Control Based on MPC and Particle Swarm Optimization Algorithm 基于MPC和粒子群优化算法的多智能体车辆编队控制
2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) Pub Date : 2022-03-04 DOI: 10.1109/ITOEC53115.2022.9734371
Jie Huang, Zhaohua Ji, Shan Xiao, Chunxia Jia, Yue Jia, Xuelei Wang
{"title":"Multi-Agent Vehicle Formation Control Based on MPC and Particle Swarm Optimization Algorithm","authors":"Jie Huang, Zhaohua Ji, Shan Xiao, Chunxia Jia, Yue Jia, Xuelei Wang","doi":"10.1109/ITOEC53115.2022.9734371","DOIUrl":"https://doi.org/10.1109/ITOEC53115.2022.9734371","url":null,"abstract":"The formation control problem of intelligent driving vehicles originates from the research on task planning and cooperation of multi-agent system, which is mainly aimed at how multi-intelligent driving vehicles cooperate to complete multi-task team driving behaviors such as formation preparation, formation maintenance, formation change and obstacle avoidance in traffic environment. From the control point of view, The vehicle queue is controlled by a plurality of single vehicle nodes, and individual vehicles are controlled through information interaction among the nodes, and then coupled with each other to form a dynamic system, so that the vehicle queue forms a multi-agent system, which is modeled and analyzed to realize an adaptive formation control model based on MPC and particle swarm optimization algorithm. Finally, Collaborative control of intelligent vehicle formation is simulated to achieve coordinated control of vehicle queue based on multi-agent system.","PeriodicalId":127300,"journal":{"name":"2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131921698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
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