Direct-drive permanent magnet wind turbine has high power generation efficiency, especially in low wind speed environment, and is widely used for wind power generation. Direct-driven permanent magnet wind turbines show no inertia response to the system through the grid connection of full-power converters, resulting in increased frequency fluctuation, poor response effect and reduced stability time of the system under sudden load and sudden wind speed conditions. Based on this, an inertia control method of direct-drive permanent magnet wind turbine under high wind power penetration is proposed, and the model of direct-drive permanent magnet wind turbine is built by designing functional modules to improve the synchronous control effect under high wind power penetration. The vector control calculation method is used to design the virtual inertia control parameters, and the decoupling quantity is introduced to decouple the parameters with filter inductance, so as to improve the supporting capacity of power grid frequency fluctuation. The simulation results show that the proposed method has a fast frequency response under sudden load change, and it drops to the lowest value of 49.16 Hz at 12.14 s. Under the condition of sudden change of wind speed, the system frequency rises to the highest value of 50.38 Hz at 12.94 s. It is proved that the proposed method has a certain suppression effect on the amplitude of frequency change, effectively shortens the time for the system frequency to return to steady state, and thus has more advantages.
{"title":"Inertia control method of direct drive permanent magnet wind turbine under high wind power permeability","authors":"Fangyuan Wang","doi":"10.3233/jcm-226726","DOIUrl":"https://doi.org/10.3233/jcm-226726","url":null,"abstract":"Direct-drive permanent magnet wind turbine has high power generation efficiency, especially in low wind speed environment, and is widely used for wind power generation. Direct-driven permanent magnet wind turbines show no inertia response to the system through the grid connection of full-power converters, resulting in increased frequency fluctuation, poor response effect and reduced stability time of the system under sudden load and sudden wind speed conditions. Based on this, an inertia control method of direct-drive permanent magnet wind turbine under high wind power penetration is proposed, and the model of direct-drive permanent magnet wind turbine is built by designing functional modules to improve the synchronous control effect under high wind power penetration. The vector control calculation method is used to design the virtual inertia control parameters, and the decoupling quantity is introduced to decouple the parameters with filter inductance, so as to improve the supporting capacity of power grid frequency fluctuation. The simulation results show that the proposed method has a fast frequency response under sudden load change, and it drops to the lowest value of 49.16 Hz at 12.14 s. Under the condition of sudden change of wind speed, the system frequency rises to the highest value of 50.38 Hz at 12.94 s. It is proved that the proposed method has a certain suppression effect on the amplitude of frequency change, effectively shortens the time for the system frequency to return to steady state, and thus has more advantages.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"9 1","pages":"2225-2235"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88701937","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}
Mechanical fault detection has an important influence on production schedule and efficiency. With the development of intelligent technology, more and more intelligent detection technologies are applied to mechanical fault detection. In order to detect mechanical faults more efficiently and accurately, this experiment proposes a production knowledge base model based on genetic algorithm (GA algorithm). The model uses the unique biological genetics principle of genetic algorithm to evolve the interested population, and can conduct spatial search to find the global optimal solution. By comparing the performance of GA algorithm model with other similar detection models, it is found that the model proposed in the experiment has obvious advantages in mechanical fault detection performance. The experimental results show that the maximum accuracy of the GA algorithm is 0.935, 0.074 higher than the support vector machine (SVM) model, 0.118 higher than the linear discriminant analysis (LDA) model, 0.032 higher than the random forest (RF) model, and 0.166 higher than the K nearest neighbor (KNN) model. In addition, the error value of GA algorithm is the lowest among these models, which is 0.028. This proves that the genetic algorithm model has higher diagnostic accuracy and can play an important role in mechanical fault detection.
{"title":"Genetic algorithm based production knowledge base for mechanical fault detection model","authors":"Yang Shen","doi":"10.3233/jcm-226719","DOIUrl":"https://doi.org/10.3233/jcm-226719","url":null,"abstract":"Mechanical fault detection has an important influence on production schedule and efficiency. With the development of intelligent technology, more and more intelligent detection technologies are applied to mechanical fault detection. In order to detect mechanical faults more efficiently and accurately, this experiment proposes a production knowledge base model based on genetic algorithm (GA algorithm). The model uses the unique biological genetics principle of genetic algorithm to evolve the interested population, and can conduct spatial search to find the global optimal solution. By comparing the performance of GA algorithm model with other similar detection models, it is found that the model proposed in the experiment has obvious advantages in mechanical fault detection performance. The experimental results show that the maximum accuracy of the GA algorithm is 0.935, 0.074 higher than the support vector machine (SVM) model, 0.118 higher than the linear discriminant analysis (LDA) model, 0.032 higher than the random forest (RF) model, and 0.166 higher than the K nearest neighbor (KNN) model. In addition, the error value of GA algorithm is the lowest among these models, which is 0.028. This proves that the genetic algorithm model has higher diagnostic accuracy and can play an important role in mechanical fault detection.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"14 1","pages":"1251-1263"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87126136","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}
In order to respond to the national goal of “carbon neutralization” and make more rational and effective use of photovoltaic resources, combined with the actual photovoltaic substation project, a fixed adjustable photovoltaic support structure design is designed. By comparing the advantages and disadvantages of the existing support, an innovative optimization design is proposed, and the mechanical structure of the support is analyzed by ANASYS to check the rationality of the design. Saving construction materials and reducing construction costs provide a basis for the reasonable design of photovoltaic power station supports, and also provide a reference for the structural design of fixed and adjustable supports.
{"title":"Structural design and simulation analysis of fixed adjustable photovoltaic support","authors":"Wen-Zhu Shen, Yawen Zeng, Weiran Zhang, Zhi Tang, Hongping Xie","doi":"10.3233/jcm-226647","DOIUrl":"https://doi.org/10.3233/jcm-226647","url":null,"abstract":"In order to respond to the national goal of “carbon neutralization” and make more rational and effective use of photovoltaic resources, combined with the actual photovoltaic substation project, a fixed adjustable photovoltaic support structure design is designed. By comparing the advantages and disadvantages of the existing support, an innovative optimization design is proposed, and the mechanical structure of the support is analyzed by ANASYS to check the rationality of the design. Saving construction materials and reducing construction costs provide a basis for the reasonable design of photovoltaic power station supports, and also provide a reference for the structural design of fixed and adjustable supports.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"5 1","pages":"1409-1423"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85520051","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}
When applied to seal liquid, magnetic fluid seal was prone to failure with the increase of shaft speed because of instability at the interface of these two fluids caused by shaft rotation. In order to avoid this problem, a new type of magnetic fluid seal was proposed, in which the magnetic fluid was separated from the sealed liquid by gas. The sealing principle of the structure was studied. Gas-liquid two-phase flow in the structure was simulated by computational fluid dynamics. A test rig of magnetic fluid seal with gas isolation was set up. Experiments of pressure resistance and seal durability of the original structure and structure with gas isolation for sealing water were carried out on the test bench. The results of theoretical analysis, CFD and experiments indicated that: there was no obvious relationship between shaft speed and performance of magnetic fluid seal when gas isolation was added for sealing water. Its pressure resistance was almost the same as that of the structure sealing gas. Its seal durability was significantly longer.
{"title":"Effect of shaft speed on performance of magnetic fluid seal with gas isolation for sealing water","authors":"Hujun Wang","doi":"10.3233/jcm-226651","DOIUrl":"https://doi.org/10.3233/jcm-226651","url":null,"abstract":"When applied to seal liquid, magnetic fluid seal was prone to failure with the increase of shaft speed because of instability at the interface of these two fluids caused by shaft rotation. In order to avoid this problem, a new type of magnetic fluid seal was proposed, in which the magnetic fluid was separated from the sealed liquid by gas. The sealing principle of the structure was studied. Gas-liquid two-phase flow in the structure was simulated by computational fluid dynamics. A test rig of magnetic fluid seal with gas isolation was set up. Experiments of pressure resistance and seal durability of the original structure and structure with gas isolation for sealing water were carried out on the test bench. The results of theoretical analysis, CFD and experiments indicated that: there was no obvious relationship between shaft speed and performance of magnetic fluid seal when gas isolation was added for sealing water. Its pressure resistance was almost the same as that of the structure sealing gas. Its seal durability was significantly longer.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"40 1","pages":"1125-1134"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84225968","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}
In order to improve the effect of landscape design, based on the traditional multi-dimensional nonlinear landscape design and RBF neural network, this paper proposes and designs a multi-dimensional nonlinear landscape design method based on neural network. Firstly, the camera parameters are set, the landscape images are collected by UAV, and the collected landscape images are segmented. Landscape image features are extracted according to different classification criteria, and the feature information is used as training samples to train the neural network. Finally, the landscape design parameters are fitted and the results of the landscape design model are output. The experimental results show that the proposed method has better classification accuracy than the other two traditional landscape image classification algorithms. In different experiments, the landscape image classification accuracy of this method is kept above 85%, while the other two methods are lower. In addition, the regression analysis value and test value of this method also perform well. Finally, given a noisy image, it is found that the text method can effectively remove the noise in the landscape design image, making the image present a clearer landscape layout.
{"title":"Neural network based multi-dimensional and nonlinear landscape design","authors":"Yang Chen, Yihuai Xie","doi":"10.3233/jcm-226724","DOIUrl":"https://doi.org/10.3233/jcm-226724","url":null,"abstract":"In order to improve the effect of landscape design, based on the traditional multi-dimensional nonlinear landscape design and RBF neural network, this paper proposes and designs a multi-dimensional nonlinear landscape design method based on neural network. Firstly, the camera parameters are set, the landscape images are collected by UAV, and the collected landscape images are segmented. Landscape image features are extracted according to different classification criteria, and the feature information is used as training samples to train the neural network. Finally, the landscape design parameters are fitted and the results of the landscape design model are output. The experimental results show that the proposed method has better classification accuracy than the other two traditional landscape image classification algorithms. In different experiments, the landscape image classification accuracy of this method is kept above 85%, while the other two methods are lower. In addition, the regression analysis value and test value of this method also perform well. Finally, given a noisy image, it is found that the text method can effectively remove the noise in the landscape design image, making the image present a clearer landscape layout.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"273 1","pages":"1279-1293"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77048724","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}
Purpose: To solve the problems of low integration accuracy and long integration time of traditional prefabricated construction information integration methods. Method: A method of assembling building information integration based on BIM and RFID technology was proposed. By analyzing the information integration principle of BIM RFID (Building Information Modeling Radio Frequency Identification) technology, starting with rfid technology, we use rfid technology to collect the information of prefabricated building components and obtain the coding information of component data. Experiment: Combining Markov model and fuzzy algorithm, the obtained coding information is preprocessed. According to the processing results, statistical feature clustering algorithm is introduced to integrate the construction information of prefabricated buildings. Result: The precision polyline of the prefabricated building construction information integration method based on BIM and RFID technology showed a steady increase, and it was close to 100% in the later stage. At the same time, the time consumed by this method was within 0.41 s, with high accuracy, high efficiency and high practicability.
目的:解决传统装配式建筑信息集成方法集成精度低、集成时间长等问题。方法:提出了一种基于BIM和RFID技术的建筑信息集成组装方法。通过分析BIM RFID (Building information Modeling Radio Frequency Identification，建筑信息建模射频识别)技术的信息集成原理，从RFID技术入手，利用RFID技术采集预制建筑构件的信息，获得构件数据的编码信息。实验:将马尔可夫模型与模糊算法相结合，对得到的编码信息进行预处理。根据处理结果，引入统计特征聚类算法对装配式建筑施工信息进行整合。结果:基于BIM和RFID技术的装配式建筑施工信息集成方法的精度折线稳步提高，后期接近100%。同时，该方法耗时在0.41 s以内，精度高、效率高、实用性强。
{"title":"Integrated application of prefabricated building construction information based on BIM and RFID technology","authors":"H. Wang","doi":"10.3233/jcm-226720","DOIUrl":"https://doi.org/10.3233/jcm-226720","url":null,"abstract":"Purpose: To solve the problems of low integration accuracy and long integration time of traditional prefabricated construction information integration methods. Method: A method of assembling building information integration based on BIM and RFID technology was proposed. By analyzing the information integration principle of BIM RFID (Building Information Modeling Radio Frequency Identification) technology, starting with rfid technology, we use rfid technology to collect the information of prefabricated building components and obtain the coding information of component data. Experiment: Combining Markov model and fuzzy algorithm, the obtained coding information is preprocessed. According to the processing results, statistical feature clustering algorithm is introduced to integrate the construction information of prefabricated buildings. Result: The precision polyline of the prefabricated building construction information integration method based on BIM and RFID technology showed a steady increase, and it was close to 100% in the later stage. At the same time, the time consumed by this method was within 0.41 s, with high accuracy, high efficiency and high practicability.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"2008 1","pages":"1265-1278"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86234875","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}
When using the support vector regression method to predict grain storage temperature, it is challenging to choose the appropriate model parameters. Generally, it is effective to examine the trend of grain storage temperature in different layers after ventilation intervention. To enhance the performance of a support vector machine, it is necessary to choose an appropriate parameter optimization algorithm. The adaptive particle swarm optimization algorithm completes the operation by continuously updating the particles in the spatial domain; after discussing its application principle in detail, the convergence effect is more optimal; and the algorithms are applied to parameter optimization for support vector regression models. After employing the adaptive particle swarm optimization algorithm, the evaluation indicators and experimental prediction results demonstrate that the APSO model has fewer errors, a higher tracking degree, superior generalization performance, and greater prediction accuracy. This is a useful resource for forecasting grain temperature trends.
{"title":"Research on grain-stored temperature prediction model based on improved SVR algorithm","authors":"Zhihui Li, Yiyi Si, Yuhua Zhu","doi":"10.3233/jcm-226642","DOIUrl":"https://doi.org/10.3233/jcm-226642","url":null,"abstract":"When using the support vector regression method to predict grain storage temperature, it is challenging to choose the appropriate model parameters. Generally, it is effective to examine the trend of grain storage temperature in different layers after ventilation intervention. To enhance the performance of a support vector machine, it is necessary to choose an appropriate parameter optimization algorithm. The adaptive particle swarm optimization algorithm completes the operation by continuously updating the particles in the spatial domain; after discussing its application principle in detail, the convergence effect is more optimal; and the algorithms are applied to parameter optimization for support vector regression models. After employing the adaptive particle swarm optimization algorithm, the evaluation indicators and experimental prediction results demonstrate that the APSO model has fewer errors, a higher tracking degree, superior generalization performance, and greater prediction accuracy. This is a useful resource for forecasting grain temperature trends.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"6 1","pages":"1547-1559"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87911174","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}
Yafei Yang, Guo-Zhen Wang, Li Wang, Yinsheng Chen, Zhizheng Shen
In this experiment, the linear CCD mixing concentration online detection device was studied under five concentrations of carmine solution: 0.1 g/L, 0.3 g/L, 0.5 g/L, 0.7 g/L, and 0.9 g/L, for the factors that can affect the detection accuracy in the real spraying process (spray flow rate, spray pressure, liquid temperature, and light intensity). The results show the following results: different spray flow rates have less influence on the concentration detection results; the greater the concentration of the solution, the less the influence of the spray pressure on the detection; the smaller the concentration of the solution, the greater the influence of the spray pressure on the detection; the greater the concentration of the solution, the greater the influence of the liquid temperature on the detection; the smaller the concentration of the solution, the greater the influence of the liquid temperature on the detection; the smaller the concentration of the solution, the greater the influence of the liquid less.
{"title":"Study on the influence of different factors on linear CCD online detection device for drug mixing concentration","authors":"Yafei Yang, Guo-Zhen Wang, Li Wang, Yinsheng Chen, Zhizheng Shen","doi":"10.3233/jcm-226670","DOIUrl":"https://doi.org/10.3233/jcm-226670","url":null,"abstract":"In this experiment, the linear CCD mixing concentration online detection device was studied under five concentrations of carmine solution: 0.1 g/L, 0.3 g/L, 0.5 g/L, 0.7 g/L, and 0.9 g/L, for the factors that can affect the detection accuracy in the real spraying process (spray flow rate, spray pressure, liquid temperature, and light intensity). The results show the following results: different spray flow rates have less influence on the concentration detection results; the greater the concentration of the solution, the less the influence of the spray pressure on the detection; the smaller the concentration of the solution, the greater the influence of the spray pressure on the detection; the greater the concentration of the solution, the greater the influence of the liquid temperature on the detection; the smaller the concentration of the solution, the greater the influence of the liquid temperature on the detection; the smaller the concentration of the solution, the greater the influence of the liquid less.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"68 1","pages":"1719-1730"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77520410","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}
The adaptive backstepping control method of permanent magnet motor has the problems of complicated coordinate transformation process and high position tracking error. Based on this, an adaptive backstepping control method of permanent magnet synchronous motor based on RBF is proposed. According to the principle of electrical machinery, the electromagnetic wave and magnetic field data are obtained, and the mathematical model of permanent magnet synchronous motor is constructed. Under the condition of keeping the resultant magnetomotive force after coordinate transformation unchanged, the structure of motor torque neural network is established by RBF method, and the coordinate transformation process is optimized. Through the compensation control strategy, the adaptive backstepping control mode is designed to realize the adaptive backstepping control of permanent magnet synchronous motor. The simulation results show that the position tracking error of the proposed method is 4.549 mm when the running time is 7 s and 43.699 mm when the running time is 14 s, which proves that the adaptive backstepping control effect of the proposed method is better.
{"title":"Adaptive backsliding control method of permanent magnet synchronous motor based on RBF","authors":"Fang Wang","doi":"10.3233/jcm-226728","DOIUrl":"https://doi.org/10.3233/jcm-226728","url":null,"abstract":"The adaptive backstepping control method of permanent magnet motor has the problems of complicated coordinate transformation process and high position tracking error. Based on this, an adaptive backstepping control method of permanent magnet synchronous motor based on RBF is proposed. According to the principle of electrical machinery, the electromagnetic wave and magnetic field data are obtained, and the mathematical model of permanent magnet synchronous motor is constructed. Under the condition of keeping the resultant magnetomotive force after coordinate transformation unchanged, the structure of motor torque neural network is established by RBF method, and the coordinate transformation process is optimized. Through the compensation control strategy, the adaptive backstepping control mode is designed to realize the adaptive backstepping control of permanent magnet synchronous motor. The simulation results show that the position tracking error of the proposed method is 4.549 mm when the running time is 7 s and 43.699 mm when the running time is 14 s, which proves that the adaptive backstepping control effect of the proposed method is better.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"58 1","pages":"1295-1305"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88865226","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}
Xianzheng Fan, Xiongfeng Jiao, Mingming Shuai, Yi Qin, Jun Chen
Railway transportation is the main means of transportation for people and the main way of logistics transportation, playing an important role in daily life. Therefore, the safety inspection of railway track has been widely valued. The abnormal intelligent detection of rail fasteners is the key content of rail safety detection. The traditional rail fastener detection method is based on machine learning for image recognition, such as SVM, to detect abnormal rail fasteners. But the traditional method has two defects. The first point is that the detection time is long, and the second point is that the detection accuracy is low. To solve this problem, a rail fastener anomaly detection model based on SVM optimized by IFOA algorithm is proposed. Firstly, the image of rail fastener is collected and filtered; Then, edge detection and image segmentation are performed to obtain the image of the target area; Finally, the HOG feature and LBP feature of the image are extracted, and the improved IFOA-SVM is used to recognize and classify the features, so as to achieve intelligent rail fastener anomaly detection. The experimental results show that when the IACO-SVM model is iterated to 254 times, the fitness value tends to be stable, which is 0.24. The detection accuracy of the model reaches 99.82%, which is higher than the traditional models, and can meet the work requirements of rail fastener anomaly detection. The rail fastener anomaly detection model based on SVM can improve the efficiency of rail fastener anomaly detection, and has a positive effect on the normal operation of railway transportation. However, the number of experimental samples used in the study is limited, which may lead to some errors in the experimental results. Therefore, it is necessary to increase the number of samples in subsequent studies.
{"title":"Application research of image recognition technology based on improved SVM in abnormal monitoring of rail fasteners","authors":"Xianzheng Fan, Xiongfeng Jiao, Mingming Shuai, Yi Qin, Jun Chen","doi":"10.3233/jcm-226723","DOIUrl":"https://doi.org/10.3233/jcm-226723","url":null,"abstract":"Railway transportation is the main means of transportation for people and the main way of logistics transportation, playing an important role in daily life. Therefore, the safety inspection of railway track has been widely valued. The abnormal intelligent detection of rail fasteners is the key content of rail safety detection. The traditional rail fastener detection method is based on machine learning for image recognition, such as SVM, to detect abnormal rail fasteners. But the traditional method has two defects. The first point is that the detection time is long, and the second point is that the detection accuracy is low. To solve this problem, a rail fastener anomaly detection model based on SVM optimized by IFOA algorithm is proposed. Firstly, the image of rail fastener is collected and filtered; Then, edge detection and image segmentation are performed to obtain the image of the target area; Finally, the HOG feature and LBP feature of the image are extracted, and the improved IFOA-SVM is used to recognize and classify the features, so as to achieve intelligent rail fastener anomaly detection. The experimental results show that when the IACO-SVM model is iterated to 254 times, the fitness value tends to be stable, which is 0.24. The detection accuracy of the model reaches 99.82%, which is higher than the traditional models, and can meet the work requirements of rail fastener anomaly detection. The rail fastener anomaly detection model based on SVM can improve the efficiency of rail fastener anomaly detection, and has a positive effect on the normal operation of railway transportation. However, the number of experimental samples used in the study is limited, which may lead to some errors in the experimental results. Therefore, it is necessary to increase the number of samples in subsequent studies.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"1 1","pages":"1307-1319"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89800803","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：