Pub Date : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046642
Hao Chen, Yang Bai, Miaomiao Wei
Taking the construction of knowledge atlas as the main line, this paper introduces the concept definition, architecture and construction technology of knowledge atlas, discusses the problems and challenges in the iterative process of constructing knowledge atlas, and finally looks forward to the possible research directions in the future. In order to help interested readers fully understand and understand the technology.
{"title":"Research and Development Trend of Knowledge Graph Construction Technology","authors":"Hao Chen, Yang Bai, Miaomiao Wei","doi":"10.1109/ICARCE55724.2022.10046642","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046642","url":null,"abstract":"Taking the construction of knowledge atlas as the main line, this paper introduces the concept definition, architecture and construction technology of knowledge atlas, discusses the problems and challenges in the iterative process of constructing knowledge atlas, and finally looks forward to the possible research directions in the future. In order to help interested readers fully understand and understand the technology.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126125544","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046546
Yonggong Han, Wen-Chung Liao, Jianxin Wang
Macrofungi refer to fungi with large fruiting bodies. In terms of systematic classification, the species of macrofungi come from the Discomycetes of Basidiomycota and Ascomycota. As the decomposer of nature, macrofungi play a key role in the carbon cycle of the earth and have strong research significance. However, there are many kinds of macrofungi, with a population of more than 10 000. It requires profound professional knowledge to identify them, which is a waste of manpower and material resources. In this study, we creatively proposed a new method based on convolutional neural network (CNN) to recognize macrofungi images. By combining the attention mechanism with the lightweight backbone model densely connected convolutional network (DenseNet), A-DenseNet model is proposed to complete the efficient classification task of macrofungi. The recognition accuracy of our model on the public macrofungi dataset reached 84.3%, and the recognition accuracy on the local macrofungi dataset reached 82.2%, which illustrates the excellent performance of our network in the macrofungi recognition task. This method is an effective supplement and reference for macrofungi classification task.
{"title":"Recognition of Macrofungi by Convolutional Neural Networks with Attention Mechanism","authors":"Yonggong Han, Wen-Chung Liao, Jianxin Wang","doi":"10.1109/ICARCE55724.2022.10046546","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046546","url":null,"abstract":"Macrofungi refer to fungi with large fruiting bodies. In terms of systematic classification, the species of macrofungi come from the Discomycetes of Basidiomycota and Ascomycota. As the decomposer of nature, macrofungi play a key role in the carbon cycle of the earth and have strong research significance. However, there are many kinds of macrofungi, with a population of more than 10 000. It requires profound professional knowledge to identify them, which is a waste of manpower and material resources. In this study, we creatively proposed a new method based on convolutional neural network (CNN) to recognize macrofungi images. By combining the attention mechanism with the lightweight backbone model densely connected convolutional network (DenseNet), A-DenseNet model is proposed to complete the efficient classification task of macrofungi. The recognition accuracy of our model on the public macrofungi dataset reached 84.3%, and the recognition accuracy on the local macrofungi dataset reached 82.2%, which illustrates the excellent performance of our network in the macrofungi recognition task. This method is an effective supplement and reference for macrofungi classification task.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115547936","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046563
Mengxia He
In response to the backwardness of traditional agricultural irrigation technology in China, many places still use traditional manual watering, which is a serious waste of water resources and low irrigation efficiency, an intelligent agricultural water-saving irrigation control system based on the Internet of Things is designed, taking into account the characteristics of the moisture content of agricultural soil in different periods. This system uses wireless communication technology to transmit the soil moisture content, dry and wet conditions and other information inside the field obtained by the soil moisture detector to the control system in real time. On this basis, the control information is sent to each irrigation area using the wireless network to control the automatic valve opening and closing of the irrigation system, thus realizing automatic water supply and water-saving irrigation.
{"title":"Research on Rural Water-saving Intelligent Irrigation System Based on Internet of Things","authors":"Mengxia He","doi":"10.1109/ICARCE55724.2022.10046563","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046563","url":null,"abstract":"In response to the backwardness of traditional agricultural irrigation technology in China, many places still use traditional manual watering, which is a serious waste of water resources and low irrigation efficiency, an intelligent agricultural water-saving irrigation control system based on the Internet of Things is designed, taking into account the characteristics of the moisture content of agricultural soil in different periods. This system uses wireless communication technology to transmit the soil moisture content, dry and wet conditions and other information inside the field obtained by the soil moisture detector to the control system in real time. On this basis, the control information is sent to each irrigation area using the wireless network to control the automatic valve opening and closing of the irrigation system, thus realizing automatic water supply and water-saving irrigation.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116122410","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046539
Chunxu Li, Jianan Luo, Jie Wen
Ship autonomous navigation is an advanced stage of ship intelligent navigation. Domestic and foreign institutions have made many efforts in the technical progress of intelligent navigation development. Before the fully autonomous navigation technology is mature, autonomous ships need to rely on remote driving and monitoring platforms to monitor and control ships. In order to achieve the progress of the ship intelligent navigation, proposed the architecture construction ideas of remote driving and monitoring platforms. The platform construction ideas are provided from the perspective of platform positioning, platform functions, overall architecture, system design and information interaction. The construction of the platform is helpful to provide development direction and experimental verification platform for domestic and foreign scientific research scholars and engineers.
{"title":"Ship Remote Driving and Monitoring Platform Architecture Construction Research","authors":"Chunxu Li, Jianan Luo, Jie Wen","doi":"10.1109/ICARCE55724.2022.10046539","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046539","url":null,"abstract":"Ship autonomous navigation is an advanced stage of ship intelligent navigation. Domestic and foreign institutions have made many efforts in the technical progress of intelligent navigation development. Before the fully autonomous navigation technology is mature, autonomous ships need to rely on remote driving and monitoring platforms to monitor and control ships. In order to achieve the progress of the ship intelligent navigation, proposed the architecture construction ideas of remote driving and monitoring platforms. The platform construction ideas are provided from the perspective of platform positioning, platform functions, overall architecture, system design and information interaction. The construction of the platform is helpful to provide development direction and experimental verification platform for domestic and foreign scientific research scholars and engineers.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123053785","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046637
Weida Ni, Y. Lou, Xiaolu Xu, Z. Shan, Shouzhong Xue, Wei Wang
Seismic random noise reduction is a critical task for seismic data processing. However, because seismic data is a representatively broadband signal, it is challenging to distinguish and attenuate random noise that present throughout the whole frequency range. Furthermore, it might be challenging to adjust the settings of denoising methods. To overcome the aforementioned problems, we suggest a multichannel filtering approach, i.e., noise-assisted multi-variate empirical mode decomposition (NA-MEMD) based multichannel singular spectrum analysis (MSSA). To filter noisy seismic data, we use NA-MEMD to decompose the noisy seismic data into a number of band-limited intrinsic mode functions (IMFs) with various center frequencies and bandwidths. Note that multiple channels of a particular IMF have the same dominant frequency aids in more reduction of random noise. Then, for separating random noise and maintaining valid signals, we apply MSSA to each IMF. The denoising outcome is then achieved by adding all the filtered IMFs. To demonstrate the validity and effectiveness of the suggested approach, we apply it to synthetic and 3D real data, and compare with traditional denoising techniques.
{"title":"Seismic Random Noise Attenuation via Noise Assisted-multivariate EMD Based MSSA","authors":"Weida Ni, Y. Lou, Xiaolu Xu, Z. Shan, Shouzhong Xue, Wei Wang","doi":"10.1109/ICARCE55724.2022.10046637","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046637","url":null,"abstract":"Seismic random noise reduction is a critical task for seismic data processing. However, because seismic data is a representatively broadband signal, it is challenging to distinguish and attenuate random noise that present throughout the whole frequency range. Furthermore, it might be challenging to adjust the settings of denoising methods. To overcome the aforementioned problems, we suggest a multichannel filtering approach, i.e., noise-assisted multi-variate empirical mode decomposition (NA-MEMD) based multichannel singular spectrum analysis (MSSA). To filter noisy seismic data, we use NA-MEMD to decompose the noisy seismic data into a number of band-limited intrinsic mode functions (IMFs) with various center frequencies and bandwidths. Note that multiple channels of a particular IMF have the same dominant frequency aids in more reduction of random noise. Then, for separating random noise and maintaining valid signals, we apply MSSA to each IMF. The denoising outcome is then achieved by adding all the filtered IMFs. To demonstrate the validity and effectiveness of the suggested approach, we apply it to synthetic and 3D real data, and compare with traditional denoising techniques.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124416144","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046598
Chenjun Gao, Ganghui Bian, Yanzhi Dong, Xiaohu Yuan, Huaping Liu
When sufficient prior knowledge is lacking or manual annotation is difficult, solving the problem directly based on training samples of unknown category can greatly reduce the time cost. Therefore, we add unsupervised learning to the preliminary groundwork of image captioning for efficient image domain conversion to achieve batch generation of the required images. At the same time, more and more infrared images are being applied to assist decision making and environment perception. Generating more diverse and discriminative image captions in similar scenes will be effective in enhancing decision making and perception capabilities. Our infrared image caption model trained with reinforcement learning has satisfactory results both in terms of quantitative scores and in real scene tests.
{"title":"Infrared Image Captioning Based on Unsupervised Learning and Reinforcement Learning","authors":"Chenjun Gao, Ganghui Bian, Yanzhi Dong, Xiaohu Yuan, Huaping Liu","doi":"10.1109/ICARCE55724.2022.10046598","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046598","url":null,"abstract":"When sufficient prior knowledge is lacking or manual annotation is difficult, solving the problem directly based on training samples of unknown category can greatly reduce the time cost. Therefore, we add unsupervised learning to the preliminary groundwork of image captioning for efficient image domain conversion to achieve batch generation of the required images. At the same time, more and more infrared images are being applied to assist decision making and environment perception. Generating more diverse and discriminative image captions in similar scenes will be effective in enhancing decision making and perception capabilities. Our infrared image caption model trained with reinforcement learning has satisfactory results both in terms of quantitative scores and in real scene tests.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"AES-12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126527716","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046553
Zhisheng Lie, S. Ren, Qiong Liu
Different objects with similar spectral features are common in remote sensing images, such as trees and low-vegetation, building and roads. It is important to segment them well for urban planning, traffic navigation, and so on. However, the existing multi-level feature fusion methods ignore the relationship among all features of each level, making these objects hard to distinguish. In this paper, we propose a Dualpath Multi-level Feature Fusion Network (DMFFN) to make good use of the features of backbone. This network includes two paths to fuse the features and model the dependences between them. After getting the features from two paths, we utilize a cross-attention module to decoder them for better segmentation. Experimental results over two datasets show that DMFFN outperforms state-of-the-art methods.
{"title":"Dual-Path Multi-Level Feature Fusion Network for Semantic Segmentation of Remote Sensing Image","authors":"Zhisheng Lie, S. Ren, Qiong Liu","doi":"10.1109/ICARCE55724.2022.10046553","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046553","url":null,"abstract":"Different objects with similar spectral features are common in remote sensing images, such as trees and low-vegetation, building and roads. It is important to segment them well for urban planning, traffic navigation, and so on. However, the existing multi-level feature fusion methods ignore the relationship among all features of each level, making these objects hard to distinguish. In this paper, we propose a Dualpath Multi-level Feature Fusion Network (DMFFN) to make good use of the features of backbone. This network includes two paths to fuse the features and model the dependences between them. After getting the features from two paths, we utilize a cross-attention module to decoder them for better segmentation. Experimental results over two datasets show that DMFFN outperforms state-of-the-art methods.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133799675","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046442
Weiwei Miao, Zeng Zeng, Changzhi Teng, Rui Zhang
Lightweight edge systems are complicated due to their limited execution capabilities and communication problems. Due to the highly complex and dynamic nature of real-time tasks, scheduling services to which nodes faces the unique challenges of resource allocation and network bandwidth coordination. To address these challenges, in this paper we introduce a scheduling mechanism called RASM. Using this mechanism, intelligent real-time tasks at the lightweight edge can be efficiently assigned to the appropriate computing nodes without waiting in long queues at the edge nodes or being allocated to the cloud indiscriminately. Our approach is a novel technology for scheduling resources in the server environment. Particularly, our method defines a cache list to record the task execution location, priority and system remaining resources, so as to unload the task to the appropriate node for execution and maximize the quality of service. Our approach is scalable and efficient with the provided infrastructure resources. Our evaluation shows that over the same conditions, RASM reduces the delay by more than 15% compared with the Cloud Only environment, and solves more tasks than the Edge Only environment.
{"title":"Resource-Aware Scheduling Mechanism for Real-Time Tasks in Lightweight Edge Systems","authors":"Weiwei Miao, Zeng Zeng, Changzhi Teng, Rui Zhang","doi":"10.1109/ICARCE55724.2022.10046442","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046442","url":null,"abstract":"Lightweight edge systems are complicated due to their limited execution capabilities and communication problems. Due to the highly complex and dynamic nature of real-time tasks, scheduling services to which nodes faces the unique challenges of resource allocation and network bandwidth coordination. To address these challenges, in this paper we introduce a scheduling mechanism called RASM. Using this mechanism, intelligent real-time tasks at the lightweight edge can be efficiently assigned to the appropriate computing nodes without waiting in long queues at the edge nodes or being allocated to the cloud indiscriminately. Our approach is a novel technology for scheduling resources in the server environment. Particularly, our method defines a cache list to record the task execution location, priority and system remaining resources, so as to unload the task to the appropriate node for execution and maximize the quality of service. Our approach is scalable and efficient with the provided infrastructure resources. Our evaluation shows that over the same conditions, RASM reduces the delay by more than 15% compared with the Cloud Only environment, and solves more tasks than the Edge Only environment.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"468 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132260110","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046555
Huaying Liu, Lei Xue
In order to realize the complex operation skills learning of a UR 10 collaborative robot, we propose a dynamic-motion-primitive robot skill learning algorithm based on reinforcement learning and imitation learning. Shapes of demonstrated trajectories is re-trained with dynamic motion primitives, and the robot arm replaces the explicit coordinates to reach the target point in an exploratory manner. Experiment results show that the optimized trajectory of the robot can preserves the shape of the teach-and-fit track well.
{"title":"Robot Skill Learning Algorithm Based on Dynamic Motion Primitive","authors":"Huaying Liu, Lei Xue","doi":"10.1109/ICARCE55724.2022.10046555","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046555","url":null,"abstract":"In order to realize the complex operation skills learning of a UR 10 collaborative robot, we propose a dynamic-motion-primitive robot skill learning algorithm based on reinforcement learning and imitation learning. Shapes of demonstrated trajectories is re-trained with dynamic motion primitives, and the robot arm replaces the explicit coordinates to reach the target point in an exploratory manner. Experiment results show that the optimized trajectory of the robot can preserves the shape of the teach-and-fit track well.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131520907","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 : 2022-12-16DOI: 10.1109/ICARCE55724.2022.10046596
Kai Chen, Kun Peng, Yue Zhu, Wenhui Shi, Yewei Jie, Zhongjie Wang, Jiafeng Sheng, Jiahan Tang
Frequent power outages for meter replacement have a negative impact on some customers' normal electricity consumption, can even result in considerable financial losses for specific customers, and raise consumer complaints, so it is necessary to research technologies that can replace meters without power outages. The new type of meter box with the special wiring base plate and plug-in increases the cost of the meter box and is difficult for installed users to apply. The use of consumable type piercing tools destroys the insulation of the original wires, has a certain depletion of materials, and lacks intelligence. The bypass circuit controlled by the contactor will destroy the leakage protector in the meter box due to inconsistencies in the contactor contacts. In this study, we analyze the phenomena whereby the contactor's direct bypass destroys the leakage protector. We create a silicon-controlled auxiliary device that allows users to replace the meter without power outages. We design a non-intrusive bypass fixture for the meter box's air switch. TMS320F28335 regulates the bypass circuit's SCR group. The device's features include reuseability, seamless transition from the main circuit to the bypass circuit, metering and protection when the bypass is converted to supply, and the ability to replace meters without power outages without electricity. The function of the device has been verified in both laboratory tests and field meter replacement work, and can be promoted in the work of low voltage distribution grid meter replacement.
{"title":"A Meter Replacing Assist Device without Power Outages Based on SCR Bypass","authors":"Kai Chen, Kun Peng, Yue Zhu, Wenhui Shi, Yewei Jie, Zhongjie Wang, Jiafeng Sheng, Jiahan Tang","doi":"10.1109/ICARCE55724.2022.10046596","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046596","url":null,"abstract":"Frequent power outages for meter replacement have a negative impact on some customers' normal electricity consumption, can even result in considerable financial losses for specific customers, and raise consumer complaints, so it is necessary to research technologies that can replace meters without power outages. The new type of meter box with the special wiring base plate and plug-in increases the cost of the meter box and is difficult for installed users to apply. The use of consumable type piercing tools destroys the insulation of the original wires, has a certain depletion of materials, and lacks intelligence. The bypass circuit controlled by the contactor will destroy the leakage protector in the meter box due to inconsistencies in the contactor contacts. In this study, we analyze the phenomena whereby the contactor's direct bypass destroys the leakage protector. We create a silicon-controlled auxiliary device that allows users to replace the meter without power outages. We design a non-intrusive bypass fixture for the meter box's air switch. TMS320F28335 regulates the bypass circuit's SCR group. The device's features include reuseability, seamless transition from the main circuit to the bypass circuit, metering and protection when the bypass is converted to supply, and the ability to replace meters without power outages without electricity. The function of the device has been verified in both laboratory tests and field meter replacement work, and can be promoted in the work of low voltage distribution grid meter replacement.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124807625","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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