At present, an automatic-mechanic contact tap-changer is widely used in power system, but it can not frequently operate. In addition, arc will occur when the switch changes. In order to solve these two problems, this ...At present, an automatic-mechanic contact tap-changer is widely used in power system, but it can not frequently operate. In addition, arc will occur when the switch changes. In order to solve these two problems, this paper presented an automatic on-load voltage-regulating distributing transformer which employed non-contact solid-state relay as tap-changer, and mainly introduced its structure, basic principal, design method of each key link and experimental results. Laboratory simulation experiments informed that the scheme was feasible. It was a smooth and effective experiment device, which was practical in application.展开更多
The construction of a mountain expressway is critical to the development of the mountain economy and the creation of mountain areas.However,since the service life of many mountain expressways continues to increase,mun...The construction of a mountain expressway is critical to the development of the mountain economy and the creation of mountain areas.However,since the service life of many mountain expressways continues to increase,municipal transformation design is required to improve the smoothness of the expressway,make efficient use of space,and reduce resource waste.The author investigates the current challenges in the construction of a mountainous expressway and proposes an effective method for municipal reconstruction design of a mountainous expressway,in the hopes of assisting in the optimization and development of the mountainous expressway.展开更多
3D model classification has emerged as a significant research focus in computer vision.However,traditional convolutional neural networks(CNNs)often struggle to capture global dependencies across both height and width ...3D model classification has emerged as a significant research focus in computer vision.However,traditional convolutional neural networks(CNNs)often struggle to capture global dependencies across both height and width dimensions simultaneously,leading to limited feature representation capabilities when handling complex visual tasks.To address this challenge,we propose a novel 3D model classification network named ViT-GE(Vision Transformer with Global and Efficient Attention),which integrates Global Grouped Coordinate Attention(GGCA)and Efficient Channel Attention(ECA)mechanisms.Specifically,the Vision Transformer(ViT)is employed to extract comprehensive global features from multi-view inputs using its self-attention mechanism,effectively capturing 3D shape characteristics.To further enhance spatial feature modeling,the GGCA module introduces a grouping strategy and global context interactions.Concurrently,the ECA module strengthens inter-channel information flow,enabling the network to adaptively emphasize key features and improve feature fusion.Finally,a voting mechanism is adopted to enhance classification accuracy,robustness,and stability.Experimental results on the ModelNet10 dataset demonstrate that our method achieves a classification accuracy of 93.50%,validating its effectiveness and superior performance.展开更多
Currently,the design of transformers insulation predominantly depends on the allowable alternating current(AC)field values for insulating oil established by Weidmann in the 1980s,lacking the research under direct curr...Currently,the design of transformers insulation predominantly depends on the allowable alternating current(AC)field values for insulating oil established by Weidmann in the 1980s,lacking the research under direct current(DC)voltage for converter transformers.This study selects naphthenic oils and paraffin-based oil transformer oil as research subjects,establishing a practical measurement platform to ascertain the oil breakdown characteristics under DC voltage.Furthermore,it statistically analyses the allowable DC field values of the oil.The findings elucidate that(1)the three-parameter Weibull dis-tribution is more suitable to conduct a statistical analysis for oil breakdown probability,yielding a fitting degree up to 99.95%.(2)For a constant electrode spacing,a 14.81%voltage increment escalates the breakdown probability of the oil gap from 3.33%to 73.33%.Concurrently,an increase in electrode spacing leads to a substantial decrement in the breakdown field strength of transformer oil,with KI25X experiencing a 54.51%reduction as electrode spacing extends from 5 to 25 mm.(3)The constant terms of the allowable DC field strength for S4,KI50X,and KI25X are found to be 19.728,17.221,and 19.281,respectively.(4)A thorough analysis for differences in physicochemical properties and electrical parameters elucidates the variations in insulation properties across different transformer oils.The findings presented in this study offer essential theoretical and technical foundations for the design,evaluation,and enhancement of insulation structures in converter transformers.展开更多
Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber r...Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.展开更多
Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber r...Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.展开更多
文摘At present, an automatic-mechanic contact tap-changer is widely used in power system, but it can not frequently operate. In addition, arc will occur when the switch changes. In order to solve these two problems, this paper presented an automatic on-load voltage-regulating distributing transformer which employed non-contact solid-state relay as tap-changer, and mainly introduced its structure, basic principal, design method of each key link and experimental results. Laboratory simulation experiments informed that the scheme was feasible. It was a smooth and effective experiment device, which was practical in application.
文摘The construction of a mountain expressway is critical to the development of the mountain economy and the creation of mountain areas.However,since the service life of many mountain expressways continues to increase,municipal transformation design is required to improve the smoothness of the expressway,make efficient use of space,and reduce resource waste.The author investigates the current challenges in the construction of a mountainous expressway and proposes an effective method for municipal reconstruction design of a mountainous expressway,in the hopes of assisting in the optimization and development of the mountainous expressway.
基金funded by the project supported by the Heilongjiang Provincial Natural Science Foundation of China(Grant Number LH2022F030).
文摘3D model classification has emerged as a significant research focus in computer vision.However,traditional convolutional neural networks(CNNs)often struggle to capture global dependencies across both height and width dimensions simultaneously,leading to limited feature representation capabilities when handling complex visual tasks.To address this challenge,we propose a novel 3D model classification network named ViT-GE(Vision Transformer with Global and Efficient Attention),which integrates Global Grouped Coordinate Attention(GGCA)and Efficient Channel Attention(ECA)mechanisms.Specifically,the Vision Transformer(ViT)is employed to extract comprehensive global features from multi-view inputs using its self-attention mechanism,effectively capturing 3D shape characteristics.To further enhance spatial feature modeling,the GGCA module introduces a grouping strategy and global context interactions.Concurrently,the ECA module strengthens inter-channel information flow,enabling the network to adaptively emphasize key features and improve feature fusion.Finally,a voting mechanism is adopted to enhance classification accuracy,robustness,and stability.Experimental results on the ModelNet10 dataset demonstrate that our method achieves a classification accuracy of 93.50%,validating its effectiveness and superior performance.
基金National Natural Science Foundation of China,Grant/Award Number:52107148China Postdoctoral Science Foundation,Grant/Award Number:2020M680485Fundamental Research Funds for the Central-Universities,Grant/Award Number:2021MS004。
文摘Currently,the design of transformers insulation predominantly depends on the allowable alternating current(AC)field values for insulating oil established by Weidmann in the 1980s,lacking the research under direct current(DC)voltage for converter transformers.This study selects naphthenic oils and paraffin-based oil transformer oil as research subjects,establishing a practical measurement platform to ascertain the oil breakdown characteristics under DC voltage.Furthermore,it statistically analyses the allowable DC field values of the oil.The findings elucidate that(1)the three-parameter Weibull dis-tribution is more suitable to conduct a statistical analysis for oil breakdown probability,yielding a fitting degree up to 99.95%.(2)For a constant electrode spacing,a 14.81%voltage increment escalates the breakdown probability of the oil gap from 3.33%to 73.33%.Concurrently,an increase in electrode spacing leads to a substantial decrement in the breakdown field strength of transformer oil,with KI25X experiencing a 54.51%reduction as electrode spacing extends from 5 to 25 mm.(3)The constant terms of the allowable DC field strength for S4,KI50X,and KI25X are found to be 19.728,17.221,and 19.281,respectively.(4)A thorough analysis for differences in physicochemical properties and electrical parameters elucidates the variations in insulation properties across different transformer oils.The findings presented in this study offer essential theoretical and technical foundations for the design,evaluation,and enhancement of insulation structures in converter transformers.
基金This work was funded by the UK EPSRC[grant number:EP/S035362/1,EP/N023013/1,EP/N02334X/1]and by the Cisco Research Centre[grant number 1525381].
文摘Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.
基金funded by the UK EPSRC[grant number:EP/S035362/1,EP/N023013/1,EP/N02334X/1]by the Cisco Research Centre[grant number 1525381].
文摘Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.
