Cutting off or controlling the enemy’s power supply at critical moments or strategic locations may result in a cascade failure,thus gaining an advantage in a war.However,the exist-ing cascading failure modeling analy...Cutting off or controlling the enemy’s power supply at critical moments or strategic locations may result in a cascade failure,thus gaining an advantage in a war.However,the exist-ing cascading failure modeling analysis of interdependent net-works is insufficient for describing the load characteristics and dependencies of subnetworks,and it is difficult to use for model-ing and failure analysis of power-combat(P-C)coupling net-works.This paper considers the physical characteristics of the two subnetworks and studies the mechanism of fault propaga-tion between subnetworks and across systems.Then the surviv-ability of the coupled network is evaluated.Firstly,an integrated modeling approach for the combat system and power system is predicted based on interdependent network theory.A heteroge-neous one-way interdependent network model based on proba-bility dependence is constructed.Secondly,using the operation loop theory,a load-capacity model based on combat-loop betweenness is proposed,and the cascade failure model of the P-C coupling system is investigated from three perspectives:ini-tial capacity,allocation strategy,and failure mechanism.Thirdly,survivability indexes based on load loss rate and network sur-vival rate are proposed.Finally,the P-C coupling system is con-structed based on the IEEE 118-bus system to demonstrate the proposed method.展开更多
Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific ...Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.展开更多
Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention an...Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention and control measures.The self-potential(SP)stands out for its sensitivity to contamination plumes,offering a solution for monitoring and detecting the movement and seepage of subsurface pollutants.However,traditional SP inversion techniques heavily rely on precise subsurface resistivity information.In this study,we propose the Attention U-Net deep learning network for rapid SP inversion.By incorporating an attention mechanism,this algorithm effectively learns the relationship between array-style SP data and the location and extent of subsurface contaminated sources.We designed a synthetic landfill model with a heterogeneous resistivity structure to assess the performance of Attention U-Net deep learning network.Additionally,we conducted further validation using a laboratory model to assess its practical applicability.The results demonstrate that the algorithm is not solely dependent on resistivity information,enabling effective locating of the source distribution,even in models with intricate subsurface structures.Our work provides a promising tool for SP data processing,enhancing the applicability of this method in the field of near-subsurface environmental monitoring.展开更多
In this paper, we use the global search characteristics of genetic algorithms to help search the weight space of the neurons in the cascade-correlation architecture. The cascade-correlation learning architecture is a ...In this paper, we use the global search characteristics of genetic algorithms to help search the weight space of the neurons in the cascade-correlation architecture. The cascade-correlation learning architecture is a technique of training and building neural networks that starts with a simple network of neurons and adds additional neurons as they are needed to suit a particular problem. In our approach, instead ofmodifying the genetic algorithm to account for convergence problems, we search the weight-space using the genetic algorithm and then apply the gradient technique of Quickprop to optimize the weights. This hybrid algorithm which is a combination of genetic algorithms and cascade-correlation is applied to the two spirals problem. We also use our algorithm in the prediction of the cyclic oxidation resistance of Ni- and Co-base superalloys.展开更多
Cascading failures in infrastructure networks have serious impacts on network function.The limited capacity of net-work nodes provides a necessary condition for cascade failure.However,the network capacity cannot be i...Cascading failures in infrastructure networks have serious impacts on network function.The limited capacity of net-work nodes provides a necessary condition for cascade failure.However,the network capacity cannot be infinite in the real net-work system.Therefore,how to reasonably allocate the limited capacity resources is of great significance.In this article,we put forward a capacity allocation strategy based on community structure against cascading failure.Experimental results indi-cate that the proposed method can reduce the scale of cascade failures with higher capacity utilization compared with Motter-Lai(ML)model.The advantage of our method is more obvious in scale-free network.Furthermore,the experiment shows that the cascade effect is more obvious when the vertex load is ran-domly varying.It is known to all that the growth of network capacity can make the network more resistant to destruction,but in this paper it is found that the contribution rate of unit capacity rises first and then decreases with the growth of net-work capacity cost.展开更多
A 3D laser scanning strategy based on cascaded deep neural network is proposed for the scanning system converted from 2D Lidar with a pitching motion device. The strategy is aimed at moving target detection and monito...A 3D laser scanning strategy based on cascaded deep neural network is proposed for the scanning system converted from 2D Lidar with a pitching motion device. The strategy is aimed at moving target detection and monitoring. Combining the device characteristics, the strategy first proposes a cascaded deep neural network, which inputs 2D point cloud, color image and pitching angle. The outputs are target distance and speed classification. And the cross-entropy loss function of network is modified by using focal loss and uniform distribution to improve the recognition accuracy. Then a pitching range and speed model are proposed to determine pitching motion parameters. Finally, the adaptive scanning is realized by integral separate speed PID. The experimental results show that the accuracies of the improved network target detection box, distance and speed classification are 90.17%, 96.87% and 96.97%, respectively. The average speed error of the improved PID is 0.4239°/s, and the average strategy execution time is 0.1521 s.The range and speed model can effectively reduce the collection of useless information and the deformation of the target point cloud. Conclusively, the experimental of overall scanning strategy show that it can improve target point cloud integrity and density while ensuring the capture of target.展开更多
With society＇s increasing dependence on critical infrastructure such as power grids and communications systems, the robustness of these systems has attracted significant attention.Failure of some nodes can trigger a ...With society＇s increasing dependence on critical infrastructure such as power grids and communications systems, the robustness of these systems has attracted significant attention.Failure of some nodes can trigger a cascading failure, which completely fragments the network, necessitating recovery efforts to improve robustness of complex systems. Inspired by real-world scenarios, this paper proposes repair models after two kinds of network failures, namely complete and incomplete collapse. In both models, three kinds of repair strategies are possible, including random selection（RS）, node selection based on single network node degree（SD）, and node selection based on double network node degree（DD）. We find that the node correlation in each of the two coupled networks affects repair efficiency. Numerical simulation and analysis results suggest that the repair node ratio and repair strategies may have a significant impact on the economics of the repair process. The results of this study thus provide insight into ways to improve the robustness of coupled networks after cascading failures.展开更多
针对高压输电线路中防振锤的背景复杂、缺陷目标小及类别数量不均衡问题,提出一种改进的Cascade R-CNN(cascade region convolutional neural networks)网络模型,用于防振锤的缺陷识别.将SE(squeeze and excitation)模块嵌入ResNet-101(...针对高压输电线路中防振锤的背景复杂、缺陷目标小及类别数量不均衡问题,提出一种改进的Cascade R-CNN(cascade region convolutional neural networks)网络模型,用于防振锤的缺陷识别.将SE(squeeze and excitation)模块嵌入ResNet-101(residual network-101),以增强网络学习能力.引入FPN(feature pyramid networks)模块提取多尺度的缺陷特征.利用Focal Loss函数降低Cascade R-CNN候选区域提取模块的分类损失.实验结果表明:相对于其他4种模型,该文模型有相对高的识别准确率;识别防振锤缺陷的效果良好.因此,该文模型具有有效性.展开更多
基金supported by the National Natural Science Foundation of China(72271242)Hunan Provincial Natural Science Foundation of China for Excellent Young Scholars(2022JJ20046).
文摘Cutting off or controlling the enemy’s power supply at critical moments or strategic locations may result in a cascade failure,thus gaining an advantage in a war.However,the exist-ing cascading failure modeling analysis of interdependent net-works is insufficient for describing the load characteristics and dependencies of subnetworks,and it is difficult to use for model-ing and failure analysis of power-combat(P-C)coupling net-works.This paper considers the physical characteristics of the two subnetworks and studies the mechanism of fault propaga-tion between subnetworks and across systems.Then the surviv-ability of the coupled network is evaluated.Firstly,an integrated modeling approach for the combat system and power system is predicted based on interdependent network theory.A heteroge-neous one-way interdependent network model based on proba-bility dependence is constructed.Secondly,using the operation loop theory,a load-capacity model based on combat-loop betweenness is proposed,and the cascade failure model of the P-C coupling system is investigated from three perspectives:ini-tial capacity,allocation strategy,and failure mechanism.Thirdly,survivability indexes based on load loss rate and network sur-vival rate are proposed.Finally,the P-C coupling system is con-structed based on the IEEE 118-bus system to demonstrate the proposed method.
基金Project(2020YFC2008605)supported by the National Key Research and Development Project of ChinaProject(52072412)supported by the National Natural Science Foundation of ChinaProject(2021JJ30359)supported by the Natural Science Foundation of Hunan Province,China。
文摘Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.
基金Projects(42174170,41874145,72088101)supported by the National Natural Science Foundation of ChinaProject(CX20200228)supported by the Hunan Provincial Innovation Foundation for Postgraduate,China。
文摘Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention and control measures.The self-potential(SP)stands out for its sensitivity to contamination plumes,offering a solution for monitoring and detecting the movement and seepage of subsurface pollutants.However,traditional SP inversion techniques heavily rely on precise subsurface resistivity information.In this study,we propose the Attention U-Net deep learning network for rapid SP inversion.By incorporating an attention mechanism,this algorithm effectively learns the relationship between array-style SP data and the location and extent of subsurface contaminated sources.We designed a synthetic landfill model with a heterogeneous resistivity structure to assess the performance of Attention U-Net deep learning network.Additionally,we conducted further validation using a laboratory model to assess its practical applicability.The results demonstrate that the algorithm is not solely dependent on resistivity information,enabling effective locating of the source distribution,even in models with intricate subsurface structures.Our work provides a promising tool for SP data processing,enhancing the applicability of this method in the field of near-subsurface environmental monitoring.
文摘In this paper, we use the global search characteristics of genetic algorithms to help search the weight space of the neurons in the cascade-correlation architecture. The cascade-correlation learning architecture is a technique of training and building neural networks that starts with a simple network of neurons and adds additional neurons as they are needed to suit a particular problem. In our approach, instead ofmodifying the genetic algorithm to account for convergence problems, we search the weight-space using the genetic algorithm and then apply the gradient technique of Quickprop to optimize the weights. This hybrid algorithm which is a combination of genetic algorithms and cascade-correlation is applied to the two spirals problem. We also use our algorithm in the prediction of the cyclic oxidation resistance of Ni- and Co-base superalloys.
文摘Cascading failures in infrastructure networks have serious impacts on network function.The limited capacity of net-work nodes provides a necessary condition for cascade failure.However,the network capacity cannot be infinite in the real net-work system.Therefore,how to reasonably allocate the limited capacity resources is of great significance.In this article,we put forward a capacity allocation strategy based on community structure against cascading failure.Experimental results indi-cate that the proposed method can reduce the scale of cascade failures with higher capacity utilization compared with Motter-Lai(ML)model.The advantage of our method is more obvious in scale-free network.Furthermore,the experiment shows that the cascade effect is more obvious when the vertex load is ran-domly varying.It is known to all that the growth of network capacity can make the network more resistant to destruction,but in this paper it is found that the contribution rate of unit capacity rises first and then decreases with the growth of net-work capacity cost.
基金funded by National Natural Science Foundation of China(Grant No. 51805146)the Fundamental Research Funds for the Central Universities (Grant No. B200202221)+1 种基金Jiangsu Key R&D Program (Grant Nos. BE2018004-1, BE2018004)College Students’ Innovative Entrepreneurial Training Plan Program (Grant No. 2020102941513)。
文摘A 3D laser scanning strategy based on cascaded deep neural network is proposed for the scanning system converted from 2D Lidar with a pitching motion device. The strategy is aimed at moving target detection and monitoring. Combining the device characteristics, the strategy first proposes a cascaded deep neural network, which inputs 2D point cloud, color image and pitching angle. The outputs are target distance and speed classification. And the cross-entropy loss function of network is modified by using focal loss and uniform distribution to improve the recognition accuracy. Then a pitching range and speed model are proposed to determine pitching motion parameters. Finally, the adaptive scanning is realized by integral separate speed PID. The experimental results show that the accuracies of the improved network target detection box, distance and speed classification are 90.17%, 96.87% and 96.97%, respectively. The average speed error of the improved PID is 0.4239°/s, and the average strategy execution time is 0.1521 s.The range and speed model can effectively reduce the collection of useless information and the deformation of the target point cloud. Conclusively, the experimental of overall scanning strategy show that it can improve target point cloud integrity and density while ensuring the capture of target.
基金supported by the National Natural Science Foundation of China(60972145)the National Aerospace Science Foundation of China(20140751008)
文摘With society＇s increasing dependence on critical infrastructure such as power grids and communications systems, the robustness of these systems has attracted significant attention.Failure of some nodes can trigger a cascading failure, which completely fragments the network, necessitating recovery efforts to improve robustness of complex systems. Inspired by real-world scenarios, this paper proposes repair models after two kinds of network failures, namely complete and incomplete collapse. In both models, three kinds of repair strategies are possible, including random selection（RS）, node selection based on single network node degree（SD）, and node selection based on double network node degree（DD）. We find that the node correlation in each of the two coupled networks affects repair efficiency. Numerical simulation and analysis results suggest that the repair node ratio and repair strategies may have a significant impact on the economics of the repair process. The results of this study thus provide insight into ways to improve the robustness of coupled networks after cascading failures.
