In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This s...In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This study aims to explore the complex evolution of the space debris environment and assess the collision risks associated with spacecraft.First,a space debris environment topological network model is proposed,which incorporates interdisciplinary methods from topological networks,fluid mechanics,and spacecraft dynamics.This model enables a structured representation of the relationships among space objects and provides rapid predictions of the space debris environment.Then,a collision probability algorithm based on the topological network model is introduced.This algorithm inherits the efficiency advantages of the topological network model and has been validated for reliability through comparison with the classical ESA’s DRAMA software.Finally,based on the above models,the collision risks of constellation satellites in Low Earth Orbit(LEO)are analyzed,including both operational and deorbit processes.The study reveals that constellation satellites face a much higher risk of internal collisions with satellites from the same constellation during operations than that with other space objects.Additionally,during the satellite deorbit process,the collision risk peaks when satellites traverse the operational region of Starlink satellites.展开更多
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.展开更多
To address the challenges of ill-defined optimization objectives,difficult constraint coordination,and lack of quantitative basis for interconnection splicing and switch placement in current distribution network topol...To address the challenges of ill-defined optimization objectives,difficult constraint coordination,and lack of quantitative basis for interconnection splicing and switch placement in current distribution network topology optimization,this paper proposes a data-driven intelligent optimization method for panoramic construction of distribution network topology based on the Common Information Model(CIM).This method integrates multi-source heterogeneous data relationships-including equipment,terminals,and connection nodes-through joint analysis of multi-line CIM and hierarchical topology extraction.It automatically identifies feeder trunk paths and branch structures,incorporates inter-connection switch splicing and intelligent path optimization strategies,and performs topology opti-mization and switch placement based on the principle of minimizing outage impact.This constructs a complete,robust main-branch topology graph model.The algorithm employs depth-first search(DFS)for supply path modeling,complemented by semantic analysis of equipment attributes and hierarchical node classification to refine topology simplification.Batch testing on a dataset of 6880 medium-voltage feeders in a Central China city achieved a 98.30%successful modeling rate for complete interconnection information,with an average processing time of approximately 4.57 s per feeder.Further validation using representative overhead,cable,and hybrid lines demonstrated high consistency between the automatically generated topology and the original system diagram in node identification,path con-struction,and information annotation,confirming the algorithm's structural adaptability and engi-neering practicality.These findings provide dynamically interactive topology model support for multiple distribution network scenarios-including planning,operation,and maintenance-offering significant application and promotion value.展开更多
We investigate the impact of network topology on blocking probability in wavelength-routed networks using a dynamic traffic growth model. The dependence of blocking on different physical parameters is assessed.
基金supported by the National Level Project of China(No.KJSP2023020201)the Foundation of Science and Technology on Aerospace Flight Dynamics Laboratory of China(No.kjw6142210240202)+1 种基金the Beijing Institute of Technology Research Fund Program for Young Scholars of Chinathe Fundamental Research Funds for Central Universities of China。
文摘In recent years,the rapid development of mega-constellations has significantly exacerbated the deterioration of the space debris environment,posing substantial and escalating threats to the safety of spacecraft.This study aims to explore the complex evolution of the space debris environment and assess the collision risks associated with spacecraft.First,a space debris environment topological network model is proposed,which incorporates interdisciplinary methods from topological networks,fluid mechanics,and spacecraft dynamics.This model enables a structured representation of the relationships among space objects and provides rapid predictions of the space debris environment.Then,a collision probability algorithm based on the topological network model is introduced.This algorithm inherits the efficiency advantages of the topological network model and has been validated for reliability through comparison with the classical ESA’s DRAMA software.Finally,based on the above models,the collision risks of constellation satellites in Low Earth Orbit(LEO)are analyzed,including both operational and deorbit processes.The study reveals that constellation satellites face a much higher risk of internal collisions with satellites from the same constellation during operations than that with other space objects.Additionally,during the satellite deorbit process,the collision risk peaks when satellites traverse the operational region of Starlink satellites.
文摘Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
基金supported by the State Grid Corporation of China science and technology project funding(5400-202322560A-3-2-ZN).
文摘To address the challenges of ill-defined optimization objectives,difficult constraint coordination,and lack of quantitative basis for interconnection splicing and switch placement in current distribution network topology optimization,this paper proposes a data-driven intelligent optimization method for panoramic construction of distribution network topology based on the Common Information Model(CIM).This method integrates multi-source heterogeneous data relationships-including equipment,terminals,and connection nodes-through joint analysis of multi-line CIM and hierarchical topology extraction.It automatically identifies feeder trunk paths and branch structures,incorporates inter-connection switch splicing and intelligent path optimization strategies,and performs topology opti-mization and switch placement based on the principle of minimizing outage impact.This constructs a complete,robust main-branch topology graph model.The algorithm employs depth-first search(DFS)for supply path modeling,complemented by semantic analysis of equipment attributes and hierarchical node classification to refine topology simplification.Batch testing on a dataset of 6880 medium-voltage feeders in a Central China city achieved a 98.30%successful modeling rate for complete interconnection information,with an average processing time of approximately 4.57 s per feeder.Further validation using representative overhead,cable,and hybrid lines demonstrated high consistency between the automatically generated topology and the original system diagram in node identification,path con-struction,and information annotation,confirming the algorithm's structural adaptability and engi-neering practicality.These findings provide dynamically interactive topology model support for multiple distribution network scenarios-including planning,operation,and maintenance-offering significant application and promotion value.
文摘We investigate the impact of network topology on blocking probability in wavelength-routed networks using a dynamic traffic growth model. The dependence of blocking on different physical parameters is assessed.
