With the rapid development of hyperspectral image technology, remote sensing technology has ushered in an innovation in theory and application, and hyperspectral remote sensing images have come into being. However, due to its high data dimensionality, it is difficult for statistical classifiers to work on it, making the technology face development difficulties. Therefore, how to effectively reduce the dimensionality of hyperspectral remote sensing images has gradually become a research hotspot in this field. The study clusters bands by K-means algorithm, and then combines the least mean square algorithm in adaptive filtering and recursive least squares method, and uses this as the basis for band selection. Finally, the dimension reduction effect is verified. The experimental results show that the improved band selection method achieves an overall accuracy of over 80% and 90% in the hyperspectral datasets of Pavia University and Idian Pine respectively, with the Kappa coefficient reaching 0.9. In the overall dimensionality reduction classification of the Indianan data, the accuracy also reaches 90% and can be maintained consistently, indicating that the method has high accuracy and can effectively reduce the dimensionality of hyperspectral remote sensing images.
{"title":"Hyperspectral remote sensing image dimensionality reduction method based on adaptive filtering","authors":"Fang Xia, Shiwei Chu, Xiangguo Liu, Guodong Li","doi":"10.3233/jcm-226714","DOIUrl":"https://doi.org/10.3233/jcm-226714","url":null,"abstract":"With the rapid development of hyperspectral image technology, remote sensing technology has ushered in an innovation in theory and application, and hyperspectral remote sensing images have come into being. However, due to its high data dimensionality, it is difficult for statistical classifiers to work on it, making the technology face development difficulties. Therefore, how to effectively reduce the dimensionality of hyperspectral remote sensing images has gradually become a research hotspot in this field. The study clusters bands by K-means algorithm, and then combines the least mean square algorithm in adaptive filtering and recursive least squares method, and uses this as the basis for band selection. Finally, the dimension reduction effect is verified. The experimental results show that the improved band selection method achieves an overall accuracy of over 80% and 90% in the hyperspectral datasets of Pavia University and Idian Pine respectively, with the Kappa coefficient reaching 0.9. In the overall dimensionality reduction classification of the Indianan data, the accuracy also reaches 90% and can be maintained consistently, indicating that the method has high accuracy and can effectively reduce the dimensionality of hyperspectral remote sensing images.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"2 1","pages":"1705-1717"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80356663","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 work of traditional intangible cultural heritage analyzes the intangible cultural works in restricted scales way. So, it is difficult to multi-scale understand the closely related to intangible cultural heritage. In this paper, visual analytics approaches are proposed for the preserve and protection those historic cultural heritages, which include multi-dimensional visualization, temporal visualization, and geospatial visualization. We take national intangible cultural heritage in Heilongjiang province as an example. Through visual analysis of intangible cultural heritage in Heilongjiang province, experts can form multi-dimensional understanding the culture of Heilongjiang province, including the categories, numbers and distributions. Furthermore, those methods we proposed can effective help experts make an in-depth analysis of those intangible cultural heritage, and provide them a new insight in a comparative way.
{"title":"A visual analytics approach for national intangible cultural heritage in Heilongjiang province","authors":"N. Zhang, Xiao-Lu Zheng","doi":"10.3233/jcm-226679","DOIUrl":"https://doi.org/10.3233/jcm-226679","url":null,"abstract":"The work of traditional intangible cultural heritage analyzes the intangible cultural works in restricted scales way. So, it is difficult to multi-scale understand the closely related to intangible cultural heritage. In this paper, visual analytics approaches are proposed for the preserve and protection those historic cultural heritages, which include multi-dimensional visualization, temporal visualization, and geospatial visualization. We take national intangible cultural heritage in Heilongjiang province as an example. Through visual analysis of intangible cultural heritage in Heilongjiang province, experts can form multi-dimensional understanding the culture of Heilongjiang province, including the categories, numbers and distributions. Furthermore, those methods we proposed can effective help experts make an in-depth analysis of those intangible cultural heritage, and provide them a new insight in a comparative way.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"8 1","pages":"1625-1633"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91040401","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}
Information technology brings popularity of the Internet, which broadens college students’ knowledge. Unexpectedly, the large amounts of information has a tremendous effect on the psychological ideology of college students who are lack of social experiences. This paper discusses how to better apply web crawler technology to analyze the potential risks of college students’ psychological crisis in the Internet environment. It is quite necessary for us to give full play to the initiative of ideological and political education in colleges and universities so as to prevent some negative and non-positive information and ideas from infiltrating and eroding students. Moreover, we should also provide a better guidance on students’ positive energy behavior, by creating an atmosphere, formulating measures, strengthening intervention as well as other mental health education methods.
{"title":"Research on college students' psychological crisis intervention based on web crawler technology","authors":"Fu Liang, Longzong Wang, Lianfeng Lai","doi":"10.3233/jcm-226650","DOIUrl":"https://doi.org/10.3233/jcm-226650","url":null,"abstract":"Information technology brings popularity of the Internet, which broadens college students’ knowledge. Unexpectedly, the large amounts of information has a tremendous effect on the psychological ideology of college students who are lack of social experiences. This paper discusses how to better apply web crawler technology to analyze the potential risks of college students’ psychological crisis in the Internet environment. It is quite necessary for us to give full play to the initiative of ideological and political education in colleges and universities so as to prevent some negative and non-positive information and ideas from infiltrating and eroding students. Moreover, we should also provide a better guidance on students’ positive energy behavior, by creating an atmosphere, formulating measures, strengthening intervention as well as other mental health education methods.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"4 1","pages":"1439-1450"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82717314","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}
One of the largest water consumers among public institutions, hospitals are characterized by water usage for various functions. Therefore, the nuanced water use requires a special methodology to determine the hospital water conservation efficiency. Based on the method of “water consumption-influencing factors in the base period”, we constructed a model for calculating the water savings amount at hospitals and demonstrated its applicability by exploring two case studies. The proposed method can help hospitals to accurately calculate the amount of water savings and determine their water conservation potential, thus evaluating the effects of the implemented water conservation measures, and achieving their targets.
{"title":"A method for calculating the water savings at typical hospitals","authors":"Lan Zhang, Chunyan Zhu, Jialin Liu, Xue Bai","doi":"10.3233/jcm-226677","DOIUrl":"https://doi.org/10.3233/jcm-226677","url":null,"abstract":"One of the largest water consumers among public institutions, hospitals are characterized by water usage for various functions. Therefore, the nuanced water use requires a special methodology to determine the hospital water conservation efficiency. Based on the method of “water consumption-influencing factors in the base period”, we constructed a model for calculating the water savings amount at hospitals and demonstrated its applicability by exploring two case studies. The proposed method can help hospitals to accurately calculate the amount of water savings and determine their water conservation potential, thus evaluating the effects of the implemented water conservation measures, and achieving their targets.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"92 1","pages":"1617-1623"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85548760","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}
During the transformer winding deformation process, the leakage magnetic field around the winding will change accordingly. Therefore, it is an effective method to monitor and track the change of the leakage magnetic field and then analyze and judge the state of the transformer. This paper firstly uses Comsol Multiphysics software to establish a 110 kV transformer electromagnetic simulation calculation model. Based on the simulation results of magnetic leakage distribution, an installation plan for the internal magnetic leakage sensor of a 110 kV true transformer is determined. The measurement results of the true single short-circuit test under different working conditions verify the accuracy of the simulation model. Subsequently, a number of B-phase high-centered three-phase short circuit (H-M B) true type tests were carried out, and the relationship between the magnetic leakage distribution characteristics and the impedance change rate after each impact was analyzed. The results show that before the transformer is seriously deformed due to multiple short circuit shocks, the sensitivity of the impedance change rate to the winding deformation is low, and the first five shocks only increase from 0.11% to 0.39%. However, the difference ratio between the simulation value and the test value of magnetic flux leakage (MFL) has obvious changes in each small deformation. BX3 increases from 1.77% to 5.62%, and BX4 increases from 2.08% to 6.55%. The difference ratio of four shocks before winding deformation is more than 6%. Therefore, by monitoring the flux leakage magnetic induction intensity, when the difference ratio is greater than 6%, strengthen the vigilance, which can provide a certain basis for winding monitoring before serious deformation.
{"title":"Transformer winding deformation accompanied by magnetic flux leakage field distribution variation and its diagnostic analysis of deformation degree","authors":"Hongliang Liu, Shuguo Gao, Lu Sun, Yuan Tian","doi":"10.3233/jcm-226656","DOIUrl":"https://doi.org/10.3233/jcm-226656","url":null,"abstract":"During the transformer winding deformation process, the leakage magnetic field around the winding will change accordingly. Therefore, it is an effective method to monitor and track the change of the leakage magnetic field and then analyze and judge the state of the transformer. This paper firstly uses Comsol Multiphysics software to establish a 110 kV transformer electromagnetic simulation calculation model. Based on the simulation results of magnetic leakage distribution, an installation plan for the internal magnetic leakage sensor of a 110 kV true transformer is determined. The measurement results of the true single short-circuit test under different working conditions verify the accuracy of the simulation model. Subsequently, a number of B-phase high-centered three-phase short circuit (H-M B) true type tests were carried out, and the relationship between the magnetic leakage distribution characteristics and the impedance change rate after each impact was analyzed. The results show that before the transformer is seriously deformed due to multiple short circuit shocks, the sensitivity of the impedance change rate to the winding deformation is low, and the first five shocks only increase from 0.11% to 0.39%. However, the difference ratio between the simulation value and the test value of magnetic flux leakage (MFL) has obvious changes in each small deformation. BX3 increases from 1.77% to 5.62%, and BX4 increases from 2.08% to 6.55%. The difference ratio of four shocks before winding deformation is more than 6%. Therefore, by monitoring the flux leakage magnetic induction intensity, when the difference ratio is greater than 6%, strengthen the vigilance, which can provide a certain basis for winding monitoring before serious deformation.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"52 1","pages":"1517-1528"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80742958","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 recognition effect of laser images, this study designed an intelligent recognition method of laser images based on big data analysis technology. On the basis of setting up the laser holographic scanning device and parameters, the laser image is obtained by using the calibration method of vision system. In order to avoid the limitation of coordinate system in the process of laser image recognition, a rational function model with general attributes is constructed. Then, convolutional neural network is used to output the feature data of laser images, and Spark parallel support vector machine algorithm is used to complete the classification of laser images. Finally, the SVM classification model based on the big data analysis technology is constructed. The texture feature data can be input to quickly output the classification results of laser images, and then the intelligent classification and recognition of laser images can be realized according to the probability distribution. Experimental results show that this method can accurately identify the tiny features in laser images, and the recognition results have high peak signal-to-noise ratio and high recognition accuracy.
{"title":"Intelligent recognition method of laser image based on big data analysis technology","authors":"Cong Li","doi":"10.3233/jcm-226674","DOIUrl":"https://doi.org/10.3233/jcm-226674","url":null,"abstract":"In order to improve the recognition effect of laser images, this study designed an intelligent recognition method of laser images based on big data analysis technology. On the basis of setting up the laser holographic scanning device and parameters, the laser image is obtained by using the calibration method of vision system. In order to avoid the limitation of coordinate system in the process of laser image recognition, a rational function model with general attributes is constructed. Then, convolutional neural network is used to output the feature data of laser images, and Spark parallel support vector machine algorithm is used to complete the classification of laser images. Finally, the SVM classification model based on the big data analysis technology is constructed. The texture feature data can be input to quickly output the classification results of laser images, and then the intelligent classification and recognition of laser images can be realized according to the probability distribution. Experimental results show that this method can accurately identify the tiny features in laser images, and the recognition results have high peak signal-to-noise ratio and high recognition accuracy.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"13 1","pages":"1601-1615"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90066086","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}
Many scholars have utilized Bodalal’s mathematical model in the development of environmentally friendly building materials to describe the physical diffusion process of volatile organic compounds. In the model, the key to calculating the diffusion coefficient of volatile organic compounds in building materials is the value of the auxiliary parameter α1 and q. This paper improves the method of solving auxiliary parameter values by using extreme value theory. Several other calculation methods are also presented. The process of solving it is explained in detail, and an example is used to verify its accuracy. As these methods are very easy to learn and easy to implement on a computer, they can greatly reduce error rates, which makes them popular and useful.
{"title":"Discussion on the calculation method for formaldehyde diffusion coefficient in building materials","authors":"Xin Zhang, Yanhong Li, Qiuxuan Song","doi":"10.3233/jcm-226665","DOIUrl":"https://doi.org/10.3233/jcm-226665","url":null,"abstract":"Many scholars have utilized Bodalal’s mathematical model in the development of environmentally friendly building materials to describe the physical diffusion process of volatile organic compounds. In the model, the key to calculating the diffusion coefficient of volatile organic compounds in building materials is the value of the auxiliary parameter α1 and q. This paper improves the method of solving auxiliary parameter values by using extreme value theory. Several other calculation methods are also presented. The process of solving it is explained in detail, and an example is used to verify its accuracy. As these methods are very easy to learn and easy to implement on a computer, they can greatly reduce error rates, which makes them popular and useful.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"13 1","pages":"1529-1536"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90381430","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}
Engineering investment is the basic investment of the whole national economic development. When the project investor obtains its expected return, it may have other beneficial benefits for social organizations or people outside the subject, but the investor cannot obtain such benefits. Spillover usually occurs from three aspects: economy, technology and knowledge. The spillover effect of project investment usually brings obvious spillover effect, which has positive benefits to society, but may also produce unfavorable factors. Therefore, it is necessary to predict the project investment spillover. When it is predicted that the investment spillover will have more favorable benefits, the preparation of relevant investment funds can be started, and when it is predicted that there will be unfavorable spillover benefits, the investment in related engineering projects will be terminated. Project investment spillover effects usually have specific rules. On the basis of summarizing and analyzing historical project investment spillover effects, the specific situation of its spillover effects can be obtained, and then the rules can be learned in combination with specific algorithms to complete the project investment spillover effects. predict. The purpose of this paper is to provide investors and institutions with a valuable investment forecasting reference method, combined with the relevant theories of the investment value of engineering market-oriented enterprises, using quantitative analysis methods and quantitative analysis methods, so as to provide an investment based on data and algorithms. The spillover value forecast method supports and promotes the development and construction of national key projects. Based on the completion of the entire prediction model, this paper uses the particle swarm optimization method of the deep neural network model process studied in this paper, and based on the relevant data of 284 historical engineering investment overflow cases, the algorithm is trained and output, and then the investment overflow of each project is obtained. The relative score of the predictions, and analyzing this overflow prediction. Through the obtained comprehensive prediction score and according to the result analysis. Corresponding conclusions and future development directions are put forward to provide theoretical guidance for investors and institutions to invest in investment direction and estimate investment spillover effects.
{"title":"Prediction of engineering investment spillover effect based on neural network","authors":"Wenguang Fan","doi":"10.3233/jcm-226678","DOIUrl":"https://doi.org/10.3233/jcm-226678","url":null,"abstract":"Engineering investment is the basic investment of the whole national economic development. When the project investor obtains its expected return, it may have other beneficial benefits for social organizations or people outside the subject, but the investor cannot obtain such benefits. Spillover usually occurs from three aspects: economy, technology and knowledge. The spillover effect of project investment usually brings obvious spillover effect, which has positive benefits to society, but may also produce unfavorable factors. Therefore, it is necessary to predict the project investment spillover. When it is predicted that the investment spillover will have more favorable benefits, the preparation of relevant investment funds can be started, and when it is predicted that there will be unfavorable spillover benefits, the investment in related engineering projects will be terminated. Project investment spillover effects usually have specific rules. On the basis of summarizing and analyzing historical project investment spillover effects, the specific situation of its spillover effects can be obtained, and then the rules can be learned in combination with specific algorithms to complete the project investment spillover effects. predict. The purpose of this paper is to provide investors and institutions with a valuable investment forecasting reference method, combined with the relevant theories of the investment value of engineering market-oriented enterprises, using quantitative analysis methods and quantitative analysis methods, so as to provide an investment based on data and algorithms. The spillover value forecast method supports and promotes the development and construction of national key projects. Based on the completion of the entire prediction model, this paper uses the particle swarm optimization method of the deep neural network model process studied in this paper, and based on the relevant data of 284 historical engineering investment overflow cases, the algorithm is trained and output, and then the investment overflow of each project is obtained. The relative score of the predictions, and analyzing this overflow prediction. Through the obtained comprehensive prediction score and according to the result analysis. Corresponding conclusions and future development directions are put forward to provide theoretical guidance for investors and institutions to invest in investment direction and estimate investment spillover effects.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"124 1","pages":"1635-1650"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72652338","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 allow users to incorrectly identify images by manipulating them using deep neural networks, this paper analyses the shortcomings of deep learning for image classification. It also develops a game that uses this technique. In the game, players can select one of their preferred product categories, causing the model to classify other product categories incorrectly as the one they selected. The goal of this game is to demonstrate to players the limitations of AI. We evaluate these programs based on their overall effectiveness, user satisfaction, and achievement of their objectives. The results show that this program is a successful method for arousing curiosity and stimulating thought. They can learn to appreciate the limitations of AI and the need to prioritize AI security in their daily activities.
{"title":"Similarity attack: An adversarial attack game for image classification based on deep learning","authors":"Xuejun Tian, Xinyuan Tian, Bingqin Pan","doi":"10.3233/jcm-226660","DOIUrl":"https://doi.org/10.3233/jcm-226660","url":null,"abstract":"In order to allow users to incorrectly identify images by manipulating them using deep neural networks, this paper analyses the shortcomings of deep learning for image classification. It also develops a game that uses this technique. In the game, players can select one of their preferred product categories, causing the model to classify other product categories incorrectly as the one they selected. The goal of this game is to demonstrate to players the limitations of AI. We evaluate these programs based on their overall effectiveness, user satisfaction, and achievement of their objectives. The results show that this program is a successful method for arousing curiosity and stimulating thought. They can learn to appreciate the limitations of AI and the need to prioritize AI security in their daily activities.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"53 1","pages":"1467-1478"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86782067","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}
Robots are widely used in all walks of life, and their excellent work efficiency has been paid attention to. As the key component of robot, manipulator plays an important role in the running performance of robot. In order to effectively improve the trajectory accuracy and efficiency of the manipulator, a six degree of freedom (6-DOF) modular manipulator trajectory planning method based on polynomial interpolation is proposed, and its feasibility and effectiveness are verified by experiments. At the same time, the performance of the method is compared with two other methods of the same type. The experimental results show that the six degree of freedom modular trajectory planning method has a shorter running time, and the shortest running time is 1.62 s. Compared with the directions in previous studies, the planning trajectory of the proposed method is more practical and its accuracy is higher. In the iterative process, the running time of the proposed method is also the shortest. In addition, the minimum error of the three methods is about 1%, which is lower than the other two methods. It is concluded that the six degree of freedom modular trajectory planning method has high feasibility and performance, which is of great significance to improve the operating efficiency and stability of the robot.
{"title":"Trajectory planning method of 6-DOF modular manipulator based on polynomial interpolation","authors":"Yihua Hu, Shulin Zhang, Yanhui Chen","doi":"10.3233/jcm-226672","DOIUrl":"https://doi.org/10.3233/jcm-226672","url":null,"abstract":"Robots are widely used in all walks of life, and their excellent work efficiency has been paid attention to. As the key component of robot, manipulator plays an important role in the running performance of robot. In order to effectively improve the trajectory accuracy and efficiency of the manipulator, a six degree of freedom (6-DOF) modular manipulator trajectory planning method based on polynomial interpolation is proposed, and its feasibility and effectiveness are verified by experiments. At the same time, the performance of the method is compared with two other methods of the same type. The experimental results show that the six degree of freedom modular trajectory planning method has a shorter running time, and the shortest running time is 1.62 s. Compared with the directions in previous studies, the planning trajectory of the proposed method is more practical and its accuracy is higher. In the iterative process, the running time of the proposed method is also the shortest. In addition, the minimum error of the three methods is about 1%, which is lower than the other two methods. It is concluded that the six degree of freedom modular trajectory planning method has high feasibility and performance, which is of great significance to improve the operating efficiency and stability of the robot.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"179 1","pages":"1589-1600"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88076359","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}
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