Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types o...Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.展开更多
We deal with the properties of incompressible and pairwise incompressible surfaces in knot complements through the application of relevant properties of almost simple topological graphs.We analyze the topological grap...We deal with the properties of incompressible and pairwise incompressible surfaces in knot complements through the application of relevant properties of almost simple topological graphs.We analyze the topological graph invariants associated with surfaces embedded in the complements of alternating and almost alternating knots.Specifically,we prove that the characteristic numbers of these graphs remain invariant under two fundamental transformations(R-move and S^(2)-move).Leveraging the interplay between characteristic numbers and Euler characteristics,and further connecting Euler characteristics to surface genus,we derive novel results regarding the genus of incompressible pairwise incompressible surfaces.Additionally,we establish a discriminant criterion to determine when such surfaces in knot complements admit genus zero.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
Objective To configure the complex traditional Chinese medicine(TCM)prescription using digit topology circle and to derive digit topology circle.Methods The basic digit topology circles were constructed.Different digi...Objective To configure the complex traditional Chinese medicine(TCM)prescription using digit topology circle and to derive digit topology circle.Methods The basic digit topology circles were constructed.Different digit topology circles were derived using basic digit topology circle,the character strings,and the digit groups.Different digit topology circles with ternary Chinese medicine were derived by adding ternary Chinese medicine into digit topology circles.The valuable TCM prescriptions were configured using the derived digit topology circles.Results Nine simple basic digit topology circles were constructed from the character strings.Multiple digit topology circles and some digit topology circles with ternary Chinese medicine were derived using basic digit topology circles,the character strings,and the digit groups.Four complex TCM prescriptions were configured using four derived digit topology circles digit topology circles,respectively.Conclusion The digit topology circles can be used to configure some existing TCM prescriptions and many novel TCM prescriptions.It has been verified that some existing TCM prescriptions have been used successfully to treat patients with diseases.Some novel valuable TCM prescriptions configured by digit topology circles may be used to treat patients with diseases.展开更多
Aiming at the problem that the joint point partition strategy expresses the important information of the human body in the human body behavior recognition of bones cannot fully express the behavior,anRCTR-GCNhuman bon...Aiming at the problem that the joint point partition strategy expresses the important information of the human body in the human body behavior recognition of bones cannot fully express the behavior,anRCTR-GCNhuman bone behavior recognition model of the correlation strategy is proposed.First,by adding an association strategy of a refined graph convolutional network model(CTR-GCN)of the smart channel topology,it can dynamically learn different topological structures and efficiently amplify the characteristics of the connection points in different channels while improving the key joint points of associated characteristics.Then,the network model redefines each channel by learning a shared topology and uses a specific channel relationship to unify the model through theoretical analysis;finally,redefining the model structure effectively reflects the associated information of local nodeswithin the channel.Action recognition has stronger aggregation capabilities.The results show that the recognition accuracy in the commonly used NTU RGB+D and NW-UCLA datasets reaches 93.6%(X-View),97.6%(XSub),and 97.2%,respectively.The experimental results show that the accuracy rate is improved.展开更多
文摘Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.
基金Supported by the National Natural Science Foundation of China(Grant No.12026411)。
文摘We deal with the properties of incompressible and pairwise incompressible surfaces in knot complements through the application of relevant properties of almost simple topological graphs.We analyze the topological graph invariants associated with surfaces embedded in the complements of alternating and almost alternating knots.Specifically,we prove that the characteristic numbers of these graphs remain invariant under two fundamental transformations(R-move and S^(2)-move).Leveraging the interplay between characteristic numbers and Euler characteristics,and further connecting Euler characteristics to surface genus,we derive novel results regarding the genus of incompressible pairwise incompressible surfaces.Additionally,we establish a discriminant criterion to determine when such surfaces in knot complements admit genus zero.
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National Natural Science Foundation of China(91748125)National Administration of Traditional Chinese Medicine’s National Inheritance Studio Construction Project for Famous Veteran Traditional Chinese Medicine Experts([2022]75)。
文摘Objective To configure the complex traditional Chinese medicine(TCM)prescription using digit topology circle and to derive digit topology circle.Methods The basic digit topology circles were constructed.Different digit topology circles were derived using basic digit topology circle,the character strings,and the digit groups.Different digit topology circles with ternary Chinese medicine were derived by adding ternary Chinese medicine into digit topology circles.The valuable TCM prescriptions were configured using the derived digit topology circles.Results Nine simple basic digit topology circles were constructed from the character strings.Multiple digit topology circles and some digit topology circles with ternary Chinese medicine were derived using basic digit topology circles,the character strings,and the digit groups.Four complex TCM prescriptions were configured using four derived digit topology circles digit topology circles,respectively.Conclusion The digit topology circles can be used to configure some existing TCM prescriptions and many novel TCM prescriptions.It has been verified that some existing TCM prescriptions have been used successfully to treat patients with diseases.Some novel valuable TCM prescriptions configured by digit topology circles may be used to treat patients with diseases.
文摘Aiming at the problem that the joint point partition strategy expresses the important information of the human body in the human body behavior recognition of bones cannot fully express the behavior,anRCTR-GCNhuman bone behavior recognition model of the correlation strategy is proposed.First,by adding an association strategy of a refined graph convolutional network model(CTR-GCN)of the smart channel topology,it can dynamically learn different topological structures and efficiently amplify the characteristics of the connection points in different channels while improving the key joint points of associated characteristics.Then,the network model redefines each channel by learning a shared topology and uses a specific channel relationship to unify the model through theoretical analysis;finally,redefining the model structure effectively reflects the associated information of local nodeswithin the channel.Action recognition has stronger aggregation capabilities.The results show that the recognition accuracy in the commonly used NTU RGB+D and NW-UCLA datasets reaches 93.6%(X-View),97.6%(XSub),and 97.2%,respectively.The experimental results show that the accuracy rate is improved.
