Jun-Hua Jia, Maofei Wang, Yongdong Dai, Haoling Zhang, Song Gao, Shenyu Wang
Operation accidents caused by deteriorated insulators occur from time to time, which poses a direct threat to the safe and stable operation of transmission lines. Much research has been done at home and abroad on the degradation mechanism of deteriorated insulators, the electric field distribution characteristics of insulator strings and the influence of deteriorated insulators on the space electric field, but there is little research on the influence of three-phase electrification on the space electric field of insulator strings. Therefore, this paper studies the simulation and detection of electric field distribution of deteriorated insulators in three-phase transmission lines. First, the difference between three-phase electrification and single-phase electrification on the space electric field of insulator strings is simulated and analyzed, and the influence of deteriorated insulators on the space electric field distribution of insulator strings under three-phase electrification is studied. Second, based on simulation results, a detection method for deteriorated insulators in three-phase overhead trans-mission lines is proposed, and a non-contact space electric field measurement device based on Unmanned Aerial Vehicle (UAV) is developed. Finally, a Unmanned Aerial Vehicle inspection system is used to test the transmission lines in combination with an electric power department, and the simulation results and the effectiveness of the proposed detection method are verified. Results show the electric field distribution of insulator strings is obviously different between three-phase electrification and single-phase electrification, and when the detection distance is 300 mm, the proposed detection method and device can effectively identify deteriorated insulators in three-phase transmission lines.
{"title":"Research on simulation and detection of space electric field distribution of deteriorated insulators in three-phase over-head transmission lines","authors":"Jun-Hua Jia, Maofei Wang, Yongdong Dai, Haoling Zhang, Song Gao, Shenyu Wang","doi":"10.3233/jcm-226658","DOIUrl":"https://doi.org/10.3233/jcm-226658","url":null,"abstract":"Operation accidents caused by deteriorated insulators occur from time to time, which poses a direct threat to the safe and stable operation of transmission lines. Much research has been done at home and abroad on the degradation mechanism of deteriorated insulators, the electric field distribution characteristics of insulator strings and the influence of deteriorated insulators on the space electric field, but there is little research on the influence of three-phase electrification on the space electric field of insulator strings. Therefore, this paper studies the simulation and detection of electric field distribution of deteriorated insulators in three-phase transmission lines. First, the difference between three-phase electrification and single-phase electrification on the space electric field of insulator strings is simulated and analyzed, and the influence of deteriorated insulators on the space electric field distribution of insulator strings under three-phase electrification is studied. Second, based on simulation results, a detection method for deteriorated insulators in three-phase overhead trans-mission lines is proposed, and a non-contact space electric field measurement device based on Unmanned Aerial Vehicle (UAV) is developed. Finally, a Unmanned Aerial Vehicle inspection system is used to test the transmission lines in combination with an electric power department, and the simulation results and the effectiveness of the proposed detection method are verified. Results show the electric field distribution of insulator strings is obviously different between three-phase electrification and single-phase electrification, and when the detection distance is 300 mm, the proposed detection method and device can effectively identify deteriorated insulators in three-phase transmission lines.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"70 1","pages":"1451-1466"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86073402","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}
Xuejun Zhang, Susu Zhang, Zhaohui Bu, Liangdi Ma, Ju Huang
Breast cancer is the most frequent cancer and the leading cause of death among females. Diagnosis mass from mammogram correctly can reduce the unnecessary biopsy to a large extent. In this paper, we present a novel mammogram classification method combining the Random Forest and the Locally Linear Embedding (LLE) dimensionality reduction algorithm for texture features. The proposed method consists of three stages. In the first stage, preprocessing is performed to enhance the contrast and suppress the noise of the ROI images. Then, the sixteen-dimensional texture features are extracted from Grey Level Co-occurrence Matrix (GLCM) as the input dataset of LLE and being mapped into a five-dimensional subspace. Finally, a Random Forest classifier is investigated for the mammogram classification and compared with the other four classifiers (SVM, KNN, Logistic Regression, MLPC). The experimental results show that the Random Forest classifier outperforms than the others, with an average accuracy of 92.87% and the AUC value of 0.99, that indicates that the combination of LLE algorithm and Random Forest classifier is a promising method for the mammogram classification.
{"title":"Texture feature dimensionality reduction-based mammography classification using Random Forest","authors":"Xuejun Zhang, Susu Zhang, Zhaohui Bu, Liangdi Ma, Ju Huang","doi":"10.3233/jcm-226669","DOIUrl":"https://doi.org/10.3233/jcm-226669","url":null,"abstract":"Breast cancer is the most frequent cancer and the leading cause of death among females. Diagnosis mass from mammogram correctly can reduce the unnecessary biopsy to a large extent. In this paper, we present a novel mammogram classification method combining the Random Forest and the Locally Linear Embedding (LLE) dimensionality reduction algorithm for texture features. The proposed method consists of three stages. In the first stage, preprocessing is performed to enhance the contrast and suppress the noise of the ROI images. Then, the sixteen-dimensional texture features are extracted from Grey Level Co-occurrence Matrix (GLCM) as the input dataset of LLE and being mapped into a five-dimensional subspace. Finally, a Random Forest classifier is investigated for the mammogram classification and compared with the other four classifiers (SVM, KNN, Logistic Regression, MLPC). The experimental results show that the Random Forest classifier outperforms than the others, with an average accuracy of 92.87% and the AUC value of 0.99, that indicates that the combination of LLE algorithm and Random Forest classifier is a promising method for the mammogram classification.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"98 1","pages":"1537-1545"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80986935","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}
Most nodes in wireless sensor networks (WSNs) are battery powered. However, battery replacement is inconvenient, which severely limits the application field of the networks. In addition, the energy consumption of nodes is not balanced in WSNs, nodes with low energy will seriously affect data transmission capability. To solve these problems, we utilize mobile chargers (MCs) in WSNs, which can move by itself and charge low-energy nodes. Firstly, we construct a mixed integer linear programming model (MILP) to solve maximum flow problem, which is proved to be NP-hard problem. To maximize flow to the sink nodes, the BottleNeck algorithm is used to generate the initial population for the genetic algorithm. This algorithm takes path as the unit and schedules MCs to charge the lowest energy node first. Then, the improved adaptive genetic algorithm (IAGA) is utilized to simulate the natural evolution process and search for the optimal deployment location for MCs. The experiment results show that IAGA can effectively improve the maximum flow of sink node compared with other methods.
{"title":"Mobile chargers scheduling algorithm for maximum data flow in wireless sensor networks","authors":"Wei Qi, Yiting Xu, Zongqian Gao, Zhiou Xu, Zhenzhen Huang, Shuo Xiao","doi":"10.3233/jcm-226667","DOIUrl":"https://doi.org/10.3233/jcm-226667","url":null,"abstract":"Most nodes in wireless sensor networks (WSNs) are battery powered. However, battery replacement is inconvenient, which severely limits the application field of the networks. In addition, the energy consumption of nodes is not balanced in WSNs, nodes with low energy will seriously affect data transmission capability. To solve these problems, we utilize mobile chargers (MCs) in WSNs, which can move by itself and charge low-energy nodes. Firstly, we construct a mixed integer linear programming model (MILP) to solve maximum flow problem, which is proved to be NP-hard problem. To maximize flow to the sink nodes, the BottleNeck algorithm is used to generate the initial population for the genetic algorithm. This algorithm takes path as the unit and schedules MCs to charge the lowest energy node first. Then, the improved adaptive genetic algorithm (IAGA) is utilized to simulate the natural evolution process and search for the optimal deployment location for MCs. The experiment results show that IAGA can effectively improve the maximum flow of sink node compared with other methods.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"41 1","pages":"1573-1587"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83089077","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}
K. Li, Rui Zhu, Zhenguo Wang, Xiaoyu Zhou, Ming-xue Wang, Siyu Xu, Yicheng Gong
The structure of the long-span transmission tower is a typical nonlinear structure with the characteristics of great height, large line span, heavy overall weight and flexible tower body. The current design code only analyzes the traditional tower types, but the analysis of the truss structure of transmission tower is limited. Aiming at improving the design defects of the structure of long-span transmission towers, this paper uses the finite element software APDL to build the three-dimensional finite element model of a long-span transmission tower, to carry out the modal finite element analysis as well as to extract the specific parameters of each modal finite element mode: Modality, Natural frequency of vibration, Periodicity. The results show that the natural vibration period of the main machinery of this type of steel transmission tower is about 0.37–1.37 s; The structure of the long-span transmission tower has certain displacements in six degrees of freedom, in which the value of the X-dimensional displacement is the largest. There are some large displacements and local torsion in the high-order mode, combined with the results of modal analysis, so it is suggested to consider the structural improvement or external reinforcement of the weak parts of the long-span transmission tower.
{"title":"Modeling and modal analysis of the structure of long-span transmission tower","authors":"K. Li, Rui Zhu, Zhenguo Wang, Xiaoyu Zhou, Ming-xue Wang, Siyu Xu, Yicheng Gong","doi":"10.3233/jcm-226644","DOIUrl":"https://doi.org/10.3233/jcm-226644","url":null,"abstract":"The structure of the long-span transmission tower is a typical nonlinear structure with the characteristics of great height, large line span, heavy overall weight and flexible tower body. The current design code only analyzes the traditional tower types, but the analysis of the truss structure of transmission tower is limited. Aiming at improving the design defects of the structure of long-span transmission towers, this paper uses the finite element software APDL to build the three-dimensional finite element model of a long-span transmission tower, to carry out the modal finite element analysis as well as to extract the specific parameters of each modal finite element mode: Modality, Natural frequency of vibration, Periodicity. The results show that the natural vibration period of the main machinery of this type of steel transmission tower is about 0.37–1.37 s; The structure of the long-span transmission tower has certain displacements in six degrees of freedom, in which the value of the X-dimensional displacement is the largest. There are some large displacements and local torsion in the high-order mode, combined with the results of modal analysis, so it is suggested to consider the structural improvement or external reinforcement of the weak parts of the long-span transmission tower.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"35 1","pages":"1491-1501"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86693713","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 the rough signal processing of AC intelligent sensor, the effective value and initial phase of voltage/current determine the test accuracy. To improve the harmonic detection and compensation performance of the existing APF and promote the improvement of power grid power quality. The direct positioning method is used as the comparison method, and the error LMS method is proposed to obtain and test the voltage and current signals of intelligent sensors. The simulation results of error LMS method show that the accuracy of voltage RMS and initial phase value calculated by method 1 increases with the increase of the number of sampling points, while the accuracy of voltage RMS of method 2 and method 3 does not change significantly. The results of correlation analysis method show that the test accuracy of the proposed method is 1/2–1/3 of the direct definition method when the amplitude of interference noise signal is 5%, 10% and 15%. Compared with the direct definition method, the rough signal processing technology has lower sampling amount and higher test accuracy, which helps to simplify the system and save the overhead cost.
{"title":"Rough signal processing of AC power intelligent sensor under the background of smart grid","authors":"Xuetang Lei, Yaya Xie, Jinkai Lei","doi":"10.3233/jcm-226686","DOIUrl":"https://doi.org/10.3233/jcm-226686","url":null,"abstract":"In the rough signal processing of AC intelligent sensor, the effective value and initial phase of voltage/current determine the test accuracy. To improve the harmonic detection and compensation performance of the existing APF and promote the improvement of power grid power quality. The direct positioning method is used as the comparison method, and the error LMS method is proposed to obtain and test the voltage and current signals of intelligent sensors. The simulation results of error LMS method show that the accuracy of voltage RMS and initial phase value calculated by method 1 increases with the increase of the number of sampling points, while the accuracy of voltage RMS of method 2 and method 3 does not change significantly. The results of correlation analysis method show that the test accuracy of the proposed method is 1/2–1/3 of the direct definition method when the amplitude of interference noise signal is 5%, 10% and 15%. Compared with the direct definition method, the rough signal processing technology has lower sampling amount and higher test accuracy, which helps to simplify the system and save the overhead cost.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"49 1","pages":"1651-1665"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84085200","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 modern network social mode accelerates the interaction of data. Under the background of the continuous development of big data and artificial intelligence technology, the ability to collect and process data has become one of the core competitiveness of enterprises. A good data management mode can also help enterprises to achieve more information resources, and win more opportunities in the industry competition, so as to obtain more benefits. The standard definition of big data by scholars in recent years is to search and make decisions based on massive data sets, and the data that can be used for analysis is the internal data of big data. The key to big data is not the massive data set, but the method to analyze the data. At present, big data has penetrated into the development and analysis of all industries, for example, the medical industry records the personal diagnostic data of patients with different diseases. Through comparative analysis and decision-making with the diagnostic data of other historical cases, a third-party reference is provided for the treatment of current patients, thereby avoiding misjudgment and misdiagnosis. With the rapid development of electronic components and data science and technology, IoT technology has gradually entered the lives of citizens. The concept of the Internet of Everything is no longer an empty talk. Smart homes have become a must-have smart device for most homes. Other similar smart cities, smart communities, and smart building designs have also begun to adopt IoT technology, and enterprise management and monitoring have also followed the trend. Connect with IoT technology. If an enterprise wants to gain a firm foothold in the industry, it not only needs excellent manufacturing level, but also needs to carry out effective cost management, and manage costs in a more scientific way, which can gain advantages for the company’s product prices. Because if the cost management of the enterprise is successful, it can reduce unnecessary waste of funds when the enterprise produces products, thereby driving the overall operating income of the enterprise. Through big data and Internet of Things technology, it can help in all aspects of enterprise management. Combining with the management dilemma of BYD in the era of big data, this paper proposes an enterprise management and monitoring method that combines big data and Internet of Things technology, business opportunity acquisition, business quality monitoring and other aspects have greatly improved.
{"title":"Enterprise management and monitoring in the background of big data and Internet of Things","authors":"Faxian Jia","doi":"10.3233/jcm-226684","DOIUrl":"https://doi.org/10.3233/jcm-226684","url":null,"abstract":"The modern network social mode accelerates the interaction of data. Under the background of the continuous development of big data and artificial intelligence technology, the ability to collect and process data has become one of the core competitiveness of enterprises. A good data management mode can also help enterprises to achieve more information resources, and win more opportunities in the industry competition, so as to obtain more benefits. The standard definition of big data by scholars in recent years is to search and make decisions based on massive data sets, and the data that can be used for analysis is the internal data of big data. The key to big data is not the massive data set, but the method to analyze the data. At present, big data has penetrated into the development and analysis of all industries, for example, the medical industry records the personal diagnostic data of patients with different diseases. Through comparative analysis and decision-making with the diagnostic data of other historical cases, a third-party reference is provided for the treatment of current patients, thereby avoiding misjudgment and misdiagnosis. With the rapid development of electronic components and data science and technology, IoT technology has gradually entered the lives of citizens. The concept of the Internet of Everything is no longer an empty talk. Smart homes have become a must-have smart device for most homes. Other similar smart cities, smart communities, and smart building designs have also begun to adopt IoT technology, and enterprise management and monitoring have also followed the trend. Connect with IoT technology. If an enterprise wants to gain a firm foothold in the industry, it not only needs excellent manufacturing level, but also needs to carry out effective cost management, and manage costs in a more scientific way, which can gain advantages for the company’s product prices. Because if the cost management of the enterprise is successful, it can reduce unnecessary waste of funds when the enterprise produces products, thereby driving the overall operating income of the enterprise. Through big data and Internet of Things technology, it can help in all aspects of enterprise management. Combining with the management dilemma of BYD in the era of big data, this paper proposes an enterprise management and monitoring method that combines big data and Internet of Things technology, business opportunity acquisition, business quality monitoring and other aspects have greatly improved.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"22 1","pages":"1679-1690"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82609945","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}
Pub Date : 2023-01-01DOI: 10.1109/mcse.2023.3282517
Patrick Diehl, Rafael Ferreira da Silva
{"title":"Science Gateways: Accelerating Research and Education - Part I","authors":"Patrick Diehl, Rafael Ferreira da Silva","doi":"10.1109/mcse.2023.3282517","DOIUrl":"https://doi.org/10.1109/mcse.2023.3282517","url":null,"abstract":"","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"124 1","pages":"5-6"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87852614","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}
Pub Date : 2022-12-31DOI: 10.5626/jcse.2022.16.4.233
San Hong, Sangjun Park, C. Kim, Hyunjoo Song
{"title":"Power System Connectivity Visualization Using an Orthogonal Graph Layout Algorithm Based on the Space-Filling Technique","authors":"San Hong, Sangjun Park, C. Kim, Hyunjoo Song","doi":"10.5626/jcse.2022.16.4.233","DOIUrl":"https://doi.org/10.5626/jcse.2022.16.4.233","url":null,"abstract":"","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"19 1","pages":"233-243"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86031739","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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