A three-dimensional multiphase particle-in-cell(MP-PIC)method was adopted to establish a liquid-solid two-phase flow model accounting for complex fracture networks.The model was validated using physical experimental d...A three-dimensional multiphase particle-in-cell(MP-PIC)method was adopted to establish a liquid-solid two-phase flow model accounting for complex fracture networks.The model was validated using physical experimental data.On this basis,the main factors influencing proppant transport in fracture network were analyzed.The study shows that proppant transport in fracture network can be divided into three stages:initial filling,dominant channel formation and fracture network extension.These correspond to three transport patterns:patch-like accumulation near the wellbore,preferential placement along main fractures,and improved the coverage of planar placement as fluid flows into branch fractures.Higher proppant density,lower fracturing fluid viscosity,lower injection rate,and larger proppant grain size result in shorter proppant transport distance and smaller planar placement coefficient.The use of low-density,small-diameter proppant combined with high-viscosity fracturing fluid and appropriately increased injection rate can effectively enlarge the stimulated volume.A smaller angle between the main fracture and branch fractures leads to longer proppant banks,broader coverage,more uniform distribution,and better stimulation performance in branch fractures.In contrast,a larger angle increases the likelihood of proppant accumulation near the branch fracture entrance and reduces the planar placement coefficient.展开更多
The combustion and detonation processes of energetic materials exhibit remarkable complexity and ultra-fast transient characteristics.While reactive molecular dynamics has been extensively employed to investigate the ...The combustion and detonation processes of energetic materials exhibit remarkable complexity and ultra-fast transient characteristics.While reactive molecular dynamics has been extensively employed to investigate the reaction dynamics of energetic materials,its utility is often constrained to capturing only fundamental reaction events and species information,thereby limiting mechanistic investigations of complex reaction pathways.To elucidate the topological features of energetic material reaction networks and identify critical reaction pathways with high fidelity,this study presents ReacNetwork-an advanced large-scale reaction network analysis methodology that synergistically integrates complex network theory with molecular simulation techniques.Specifically,we have developed a multi-dimensional feature screening protocol based on node centrality metrics and K-shell decomposition algorithms.Takingα-Hexahydro-1,3,5-trinitro-1,3,5-triazine(α-RDX)as the subject,we successfully constructed a comprehensive high-temperature thermal decomposition reaction network consisting of 1,134 distinct chemical species and 3,626 elementary reactions.Through systematic application of community detection algorithms and global topological feature extraction techniques,we achieved effective dimensionality reduction and successfully identified the dominant reaction pathway within theα-RDX thermal decomposition network.The computational results not only validate the well-established initial reaction mechanism dominated by N-NO2 homolytic bond cleavage,but also provide unprecedented visualization ofα-RDX framework ring-opening dynamics and subsequent radical chain propagation networks.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
Network topology obfuscation is a technique aimed at protecting critical nodes and links from disruptions such as Link Flooding Attack(LFA).Currently,there are limited topology obfuscation methods for protecting criti...Network topology obfuscation is a technique aimed at protecting critical nodes and links from disruptions such as Link Flooding Attack(LFA).Currently,there are limited topology obfuscation methods for protecting critical nodes,and the existing approaches mainly achieve obfuscation by extensivelymodifying network links,resulting in high costs.To address this issue,this paper proposes a low-cost network topology obfuscation method dedicated to critical node protection,with its core innovation lying in a lightweight obfuscation architecture based on Fake Node Clusters(FNCs).Firstly,the protected network is modeled as an undirected graph,and an adjacency matrix is constructed to quantify the network scale and structural characteristics.Then,a fake node cluster generation algorithm is designed to construct an FNC adapted to the target network.Finally,a heuristic obfuscated topology generation algorithm is proposed.By optimizing the deployment positions of Fake Nodes Clusters(FNCs)in the protected network,this algorithm effectively reduces the number of FNCs required to generate the obfuscated topology,further lowering the obfuscation cost.Extensive experiments were conducted on the public Topology Zoo dataset,categorizing network topologies by node count into small-scale([0,50)),medium-scale([50,100)),and large-scale([100,200))groups.The experimental results demonstrate that the proposed approach achieves excellent obfuscation performance,reducing the critical node recognition rate to 0%.Compared to the typical method,EigenObfu,the proposed approach also reduces obfuscation costs by an average of 97.9%,99.6%,and 99.3%for small,medium,and large-scale networks,respectively.展开更多
The development of brain-computer interfaces(BCI)based on motor imagery(MI)has greatly improved patients’quality of life with movement disorders.The classification of upper limb MI has been widely studied and applied...The development of brain-computer interfaces(BCI)based on motor imagery(MI)has greatly improved patients’quality of life with movement disorders.The classification of upper limb MI has been widely studied and applied in many fields,including rehabilitation.However,the physiological representations of left and right lower limb movements are too close and activated deep in the cerebral cortex,making it difficult to distinguish their features.Therefore,classifying lower limbs motor imagery is more challenging.In this study,we propose a feature extraction method based on functional connectivity,which utilizes phase-locked values to construct a functional connectivity matrix as the features of the left and right legs,which can effectively avoid the problem of physiological representations of the left and right lower limbs being too close to each other during movement.In addition,considering the topology and the temporal characteristics of the electroencephalogram(EEG),we designed a temporal-spatial convolutional network(TSGCN)to capture the spatiotemporal information for classification.Experimental results show that the accuracy of the proposed method is higher than that of existing methods,achieving an average classification accuracy of 73.58%on the internal dataset.Finally,this study explains the network mechanism of left and right foot MI from the perspective of graph theoretic features and demonstrates the feasibility of decoding lower limb MI.展开更多
User identity linkage(UIL)across online social networks seeks to match accounts belonging to the same real-world individual.This cross-platformmapping enables accurate user modeling but also raises serious privacy ris...User identity linkage(UIL)across online social networks seeks to match accounts belonging to the same real-world individual.This cross-platformmapping enables accurate user modeling but also raises serious privacy risks.Over the past decade,the research community has developed a wide range of UIL methods,from structural embeddings tomultimodal fusion architectures.However,corresponding adversarial and defensive approaches remain fragmented and comparatively understudied.In this survey,we provide a unified overview of both mapping and antimappingmethods for UIL.We categorize representativemappingmodels by learning paradigmand datamodality,and systematically compare them with emerging countermeasures including adversarial injection,structural perturbation,and identity obfuscation.To bridge these two threads,we introduce amodality-oriented taxonomy and a formal gametheoretic framing that casts cross-network mapping as a contest between mappers and anti-mappers.This framing allows us to construct a cross-modality dependency matrix,which reveals structural information as themost contested signal,identifies node injection as the most robust defensive strategy,and points to multimodal integration as a promising direction.Our survey underscores the need for balanced,privacy-preserving identity inference and provides a foundation for future research on the adversarial dynamics of social identity mapping and defense.展开更多
Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods ex...Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.展开更多
Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these netw...Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.展开更多
A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are sys...A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are systematically analyzed,the appropriate factor model is found,and the sharing of performance benefits between institutions and employees,the change in distribution proportion,and the risk of institutional improvement and employee cooperation are considered.Second,based on the mechanism improvement and employee cooperation,the payment matrix is given and evolutionary game analysis is carried out to obtain a stable and balanced institutional improvement probability and employee cooperation probability.These two probability values are substituted into the Bayesian network model of performance improvement of new R&D institutions,and the posterior probability of performance improvement is predicted by Bayesian network reasoning and diagnosis to find effective improvement measures.Finally,practical case analysis is given to verify the effectiveness and practicability of the proposed method.展开更多
Research on neutron-induced fission product yields of^(232)Th is crucial for understanding the competition between symmetric and asymmetric fission in actinide nuclei.However,obtaining complete isotopic yield distribu...Research on neutron-induced fission product yields of^(232)Th is crucial for understanding the competition between symmetric and asymmetric fission in actinide nuclei.However,obtaining complete isotopic yield distributions over a wide range of neutron energies remains a challenge.In this study,a Bayesian neural network model was developed to predict the independent(IND)and cumulative fission yields of^(232)Th under neutron irradiation at various incident energies.To address the limited availability of experimental data for the analysis of IND mass distributions,we substituted mass-number-based yields with the yields of specific isotopes.Furthermore,physical phenomena or quantities,such as the odd-even effect and isospin,were introduced as constraints to enhance the physical consistency of the predictions.The impact of these constraints was evaluated using mass-chain yield distributions and their dependence on energy.Incorporating physical constraints significantly improves the prediction accuracy,yielding more reliable and physically meaningful fission yield data for nuclear physics and reactor design applications.展开更多
To address the problems of low accuracy by the CONWEP model and poor efficiency by the Coupled Eulerian-Lagrangian(CEL)method in predicting close-range air blast loads of cylindrical charges,a neural network-based sim...To address the problems of low accuracy by the CONWEP model and poor efficiency by the Coupled Eulerian-Lagrangian(CEL)method in predicting close-range air blast loads of cylindrical charges,a neural network-based simulation(NNS)method with higher accuracy and better efficiency was proposed.The NNS method consisted of three main steps.First,the parameters of blast loads,including the peak pressures and impulses of cylindrical charges with different aspect ratios(L/D)at different stand-off distances and incident angles were obtained by two-dimensional numerical simulations.Subsequently,incident shape factors of cylindrical charges with arbitrary aspect ratios were predicted by a neural network.Finally,reflected shape factors were derived and implemented into the subroutine of the ABAQUS code to modify the CONWEP model,including modifications of impulse and overpressure.The reliability of the proposed NNS method was verified by related experimental results.Remarkable accuracy improvement was acquired by the proposed NNS method compared with the unmodified CONWEP model.Moreover,huge efficiency superiority was obtained by the proposed NNS method compared with the CEL method.The proposed NNS method showed good accuracy when the scaled distance was greater than 0.2 m/kg^(1/3).It should be noted that there is no need to generate a new dataset again since the blast loads satisfy the similarity law,and the proposed NNS method can be directly used to simulate the blast loads generated by different cylindrical charges.The proposed NNS method with high efficiency and accuracy can be used as an effective method to analyze the dynamic response of structures under blast loads,and it has significant application prospects in designing protective structures.展开更多
Wellbore breakout is one of the critical issues in drilling due to the fact that the related problems result in additional costs and impact the drilling scheme severely.However,the majority of such wellbore breakout a...Wellbore breakout is one of the critical issues in drilling due to the fact that the related problems result in additional costs and impact the drilling scheme severely.However,the majority of such wellbore breakout analyses were based on continuum mechanics.In addition to failure in intact rocks,wellbore breakouts can also be initiated along natural discontinuities,e.g.weak planes and fractures.Furthermore,the conventional models in wellbore breakouts with uniform distribution fractures could not reflect the real drilling situation.This paper presents a fully coupled hydro-mechanical model of the SB-X well in the Tarim Basin,China for evaluating wellbore breakouts in heavily fractured rocks under anisotropic stress states using the distinct element method(DEM)and the discrete fracture network(DFN).The developed model was validated against caliper log measurement,and its stability study was carried out by stress and displacement analyses.A parametric study was performed to investigate the effects of the characteristics of fracture distribution(orientation and length)on borehole stability by sensitivity studies.Simulation results demonstrate that the increase of the standard deviation of orientation when the fracture direction aligns parallel or perpendicular to the principal stress direction aggravates borehole instability.Moreover,an elevation in the average fracture length causes the borehole failure to change from the direction of the minimum in-situ horizontal principal stress(i.e.the direction of wellbore breakouts)towards alternative directions,ultimately leading to the whole wellbore failure.These findings provide theoretical insights for predicting wellbore breakouts in heavily fractured rocks.展开更多
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based...With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.展开更多
The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic,highlighting the growing importance of network security.Intrusion Detection Systems(IDS)are essential ...The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic,highlighting the growing importance of network security.Intrusion Detection Systems(IDS)are essential for safeguarding network integrity.To address the low accuracy of existing intrusion detection models in identifying network attacks,this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network(SA-ResNet).Utilizing residual connections can effectively capture local features in the data;by introducing a spatial attention mechanism,the global dependency relationships of intrusion features can be extracted,enhancing the intrusion recognition model’s focus on the global features of intrusions,and effectively improving the accuracy of intrusion recognition.The proposed model in this paper was experimentally verified on theNSL-KDD dataset.The experimental results showthat the intrusion recognition accuracy of the intrusion detection method based on SA-ResNet has reached 99.86%,and its overall accuracy is 0.41% higher than that of traditional Convolutional Neural Network(CNN)models.展开更多
With the widespread implementation of distributed generation(DG)and the integration of soft open point(SOP)into the distribution network(DN),the latter is steadily transitioning into a flexible distribution network(FD...With the widespread implementation of distributed generation(DG)and the integration of soft open point(SOP)into the distribution network(DN),the latter is steadily transitioning into a flexible distribution network(FDN),the calculation of carbon flow distribution in FDN is more difficult.To this end,this study constructs a model for low-carbon optimal operations within the FDN on the basis of enhanced carbon emission flow(CEF).First,the carbon emission characteristics of FDNs are scrutinized and an improved method for calculating carbon flow within these networks is proposed.Subsequently,a model for optimizing low-carbon operations within FDNs is formulated based on the refined CEF,which merges the specificities of DG and intelligent SOP.Finally,this model is scrutinized using an upgraded IEEE 33-node distribution system,a comparative analysis of the cases reveals that when DG and SOP are operated in a coordinated manner in the FDN,with the cost of electricity generation was reduced by 40.63 percent and the cost of carbon emissions by 10.18 percent.The findings indicate that the judicious optimization of areas exhibiting higher carbon flow rates can effectively enhance the economic efficiency of DN operations and curtail the carbon emissions of the overall network.展开更多
Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas.At present,numerical model forecasting consumes too many ...Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas.At present,numerical model forecasting consumes too many resources and takes too long to compute,while neural network forecasting lacks regional data to train regional forecasting models.In this study,we used the DUAL wind model to build typhoon wind fields,and constructed a typhoon database of 75 processes in the northern South China Sea using the coupled Advanced Circulation-Simulating Waves Nearshore(ADCIRC-SWAN)model.Then,a neural network with a Res-U-Net structure was trained using the typhoon database to forecast the typhoon processes in the validation dataset,and an excellent storm surge forecasting effect was achieved in the Pearl River Estuary region.The storm surge forecasting effect of stronger typhoons was improved by adding a branch structure and transfer learning.展开更多
Accurate and efficient prediction of the distribution of surface loads on buildings subjected to explosive effects is crucial for rapidly calculating structural dynamic responses,establishing effective protective meas...Accurate and efficient prediction of the distribution of surface loads on buildings subjected to explosive effects is crucial for rapidly calculating structural dynamic responses,establishing effective protective measures,and designing civil defense engineering solutions.Current state-of-the-art methods face several issues:Experimental research is difficult and costly to implement,theoretical research is limited to simple geometries and lacks precision,and direct simulations require substantial computational resources.To address these challenges,this paper presents a data-driven method for predicting blast loads on building surfaces.This approach increases both the accuracy and computational efficiency of load predictions when the geometry of the building changes while the explosive yield remains constant,significantly improving its applicability in complex scenarios.This study introduces an innovative encoder-decoder graph neural network model named BlastGraphNet,which uses a message-passing mechanism to predict the overpressure and impulse load distributions on buildings with conventional and complex geometries during explosive events.The model also facilitates related downstream applications,such as damage mode identification and rapid assessment of virtual city explosions.The calculation results indicate that the prediction error of the model for conventional building tests is less than 2%,and its inference speed is 3-4 orders of magnitude faster than that of state-of-the-art numerical methods.In extreme test cases involving buildings with complex geometries and building clusters,the method achieved high accuracy and excellent generalizability.The strong adaptability and generalizability of BlastGraphNet confirm that this novel method enables precise real-time prediction of blast loads and provides a new paradigm for damage assessment in protective engineering.展开更多
Physics-informed neural networks(PINNs)have shown remarkable prospects in solving the forward and inverse problems involving partial differential equations(PDEs).The method embeds PDEs into the neural network by calcu...Physics-informed neural networks(PINNs)have shown remarkable prospects in solving the forward and inverse problems involving partial differential equations(PDEs).The method embeds PDEs into the neural network by calculating the PDE loss at a set of collocation points,providing advantages such as meshfree and more convenient adaptive sampling.However,when solving PDEs using nonuniform collocation points,PINNs still face challenge regarding inefficient convergence of PDE residuals or even failure.In this work,we first analyze the ill-conditioning of the PDE loss in PINNs under nonuniform collocation points.To address the issue,we define volume weighting residual and propose volume weighting physics-informed neural networks(VW-PINNs).Through weighting the PDE residuals by the volume that the collocation points occupy within the computational domain,we embed explicitly the distribution characteristics of collocation points in the loss evaluation.The fast and sufficient convergence of the PDE residuals for the problems involving nonuniform collocation points is guaranteed.Considering the meshfree characteristics of VW-PINNs,we also develop a volume approximation algorithm based on kernel density estimation to calculate the volume of the collocation points.We validate the universality of VW-PINNs by solving the forward problems involving flow over a circular cylinder and flow over the NACA0012 airfoil under different inflow conditions,where conventional PINNs fail.By solving the Burgers’equation,we verify that VW-PINNs can enhance the efficiency of existing the adaptive sampling method in solving the forward problem by three times,and can reduce the relative L 2 error of conventional PINNs in solving the inverse problem by more than one order of magnitude.展开更多
Heat dissipation performance is critical to the design of high-end equipment,such as integrated chips and high-precision machine tools.Owing to the advantages of artificial intelligence in solving complex tasks involv...Heat dissipation performance is critical to the design of high-end equipment,such as integrated chips and high-precision machine tools.Owing to the advantages of artificial intelligence in solving complex tasks involving a large number of variables,researchers have exploited deep learning to expedite the optimization of material properties,such as the heat dissipation of solid isotropic materials with penalization(SIMP).However,because the approach is limited by discrete datasets and labeled training forms,ensuring the continuous adaptation of the condition domain and maintaining the stability of the design structure remain major challenges in the current intelligent design methodology for thermally conductive structures.In this study,we propose an innovative intelligent design fram-ework integrating Conditional Deep Convolutional Generative Adversarial Networks(CDCGAN)with SIMP,capable of creating topology structures that meet prescribed thermal conduction performance.This proposed design strategy significantly reduces the computational time required to solve symmetric and random heat sink problems compared with existing design approaches and is approximately 98%faster than standard SIMP methods and 55.5%faster than conventional deep-learning-based methods.In addition,we benchmarked the design performance of the proposed framework against theoretical structural designs via experimental measurements.We observed a 50.1%reduction in the average temperature and a 28.2%reduction in the highest temperature in our designed topology compared with those theoretical structure designs.展开更多
The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system...The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system,the connection point of each network is regarded as a neuron in the nervous system,and each connection point has input and output functions.Only when the input of nodes reaches a certain threshold can the output function of nodes be stimulated.Using the networking mode of the artificial neural network model,the mobile node can output in multiple directions.If the input direction of a certain path is the same as that of other nodes,it can choose to avoid and choose another path.The weighted value of each path between nodes is different,which means that the influence of the front node on the current node varies.The control method based on the artificial neural network model can be applied to vehicle control,household sweeping robots,and other fields,and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.展开更多
基金Supported by the National Natural Science Foundation of China(U21B2071,U23B20156)。
文摘A three-dimensional multiphase particle-in-cell(MP-PIC)method was adopted to establish a liquid-solid two-phase flow model accounting for complex fracture networks.The model was validated using physical experimental data.On this basis,the main factors influencing proppant transport in fracture network were analyzed.The study shows that proppant transport in fracture network can be divided into three stages:initial filling,dominant channel formation and fracture network extension.These correspond to three transport patterns:patch-like accumulation near the wellbore,preferential placement along main fractures,and improved the coverage of planar placement as fluid flows into branch fractures.Higher proppant density,lower fracturing fluid viscosity,lower injection rate,and larger proppant grain size result in shorter proppant transport distance and smaller planar placement coefficient.The use of low-density,small-diameter proppant combined with high-viscosity fracturing fluid and appropriately increased injection rate can effectively enlarge the stimulated volume.A smaller angle between the main fracture and branch fractures leads to longer proppant banks,broader coverage,more uniform distribution,and better stimulation performance in branch fractures.In contrast,a larger angle increases the likelihood of proppant accumulation near the branch fracture entrance and reduces the planar placement coefficient.
基金support from the National Natural Science Foundation of China(Grant No.22275018).
文摘The combustion and detonation processes of energetic materials exhibit remarkable complexity and ultra-fast transient characteristics.While reactive molecular dynamics has been extensively employed to investigate the reaction dynamics of energetic materials,its utility is often constrained to capturing only fundamental reaction events and species information,thereby limiting mechanistic investigations of complex reaction pathways.To elucidate the topological features of energetic material reaction networks and identify critical reaction pathways with high fidelity,this study presents ReacNetwork-an advanced large-scale reaction network analysis methodology that synergistically integrates complex network theory with molecular simulation techniques.Specifically,we have developed a multi-dimensional feature screening protocol based on node centrality metrics and K-shell decomposition algorithms.Takingα-Hexahydro-1,3,5-trinitro-1,3,5-triazine(α-RDX)as the subject,we successfully constructed a comprehensive high-temperature thermal decomposition reaction network consisting of 1,134 distinct chemical species and 3,626 elementary reactions.Through systematic application of community detection algorithms and global topological feature extraction techniques,we achieved effective dimensionality reduction and successfully identified the dominant reaction pathway within theα-RDX thermal decomposition network.The computational results not only validate the well-established initial reaction mechanism dominated by N-NO2 homolytic bond cleavage,but also provide unprecedented visualization ofα-RDX framework ring-opening dynamics and subsequent radical chain propagation networks.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金funded by the National Key Research and Development Program of China(Grant No.2022YFB3102900)the Key Science and Technology Project of Henan Province,China(No.252102211091).
文摘Network topology obfuscation is a technique aimed at protecting critical nodes and links from disruptions such as Link Flooding Attack(LFA).Currently,there are limited topology obfuscation methods for protecting critical nodes,and the existing approaches mainly achieve obfuscation by extensivelymodifying network links,resulting in high costs.To address this issue,this paper proposes a low-cost network topology obfuscation method dedicated to critical node protection,with its core innovation lying in a lightweight obfuscation architecture based on Fake Node Clusters(FNCs).Firstly,the protected network is modeled as an undirected graph,and an adjacency matrix is constructed to quantify the network scale and structural characteristics.Then,a fake node cluster generation algorithm is designed to construct an FNC adapted to the target network.Finally,a heuristic obfuscated topology generation algorithm is proposed.By optimizing the deployment positions of Fake Nodes Clusters(FNCs)in the protected network,this algorithm effectively reduces the number of FNCs required to generate the obfuscated topology,further lowering the obfuscation cost.Extensive experiments were conducted on the public Topology Zoo dataset,categorizing network topologies by node count into small-scale([0,50)),medium-scale([50,100)),and large-scale([100,200))groups.The experimental results demonstrate that the proposed approach achieves excellent obfuscation performance,reducing the critical node recognition rate to 0%.Compared to the typical method,EigenObfu,the proposed approach also reduces obfuscation costs by an average of 97.9%,99.6%,and 99.3%for small,medium,and large-scale networks,respectively.
基金supported in part by the National Natural Science Foundation of China under Grant 62172368the Natural Science Foundation of Zhejiang Province under Grant LR22F020003.
文摘The development of brain-computer interfaces(BCI)based on motor imagery(MI)has greatly improved patients’quality of life with movement disorders.The classification of upper limb MI has been widely studied and applied in many fields,including rehabilitation.However,the physiological representations of left and right lower limb movements are too close and activated deep in the cerebral cortex,making it difficult to distinguish their features.Therefore,classifying lower limbs motor imagery is more challenging.In this study,we propose a feature extraction method based on functional connectivity,which utilizes phase-locked values to construct a functional connectivity matrix as the features of the left and right legs,which can effectively avoid the problem of physiological representations of the left and right lower limbs being too close to each other during movement.In addition,considering the topology and the temporal characteristics of the electroencephalogram(EEG),we designed a temporal-spatial convolutional network(TSGCN)to capture the spatiotemporal information for classification.Experimental results show that the accuracy of the proposed method is higher than that of existing methods,achieving an average classification accuracy of 73.58%on the internal dataset.Finally,this study explains the network mechanism of left and right foot MI from the perspective of graph theoretic features and demonstrates the feasibility of decoding lower limb MI.
基金funded by the National Key R&D Program of China under Grant(No.2022YFB3102901)National Natural Science Foundation of China(Nos.62072115,62102094)Shanghai Science and Technology Innovation Action Plan Project(No.22510713600).
文摘User identity linkage(UIL)across online social networks seeks to match accounts belonging to the same real-world individual.This cross-platformmapping enables accurate user modeling but also raises serious privacy risks.Over the past decade,the research community has developed a wide range of UIL methods,from structural embeddings tomultimodal fusion architectures.However,corresponding adversarial and defensive approaches remain fragmented and comparatively understudied.In this survey,we provide a unified overview of both mapping and antimappingmethods for UIL.We categorize representativemappingmodels by learning paradigmand datamodality,and systematically compare them with emerging countermeasures including adversarial injection,structural perturbation,and identity obfuscation.To bridge these two threads,we introduce amodality-oriented taxonomy and a formal gametheoretic framing that casts cross-network mapping as a contest between mappers and anti-mappers.This framing allows us to construct a cross-modality dependency matrix,which reveals structural information as themost contested signal,identifies node injection as the most robust defensive strategy,and points to multimodal integration as a promising direction.Our survey underscores the need for balanced,privacy-preserving identity inference and provides a foundation for future research on the adversarial dynamics of social identity mapping and defense.
基金This study was supported by:Inner Mongolia Academy of Forestry Sciences Open Research Project(Grant No.KF2024MS03)The Project to Improve the Scientific Research Capacity of the Inner Mongolia Academy of Forestry Sciences(Grant No.2024NLTS04)The Innovation and Entrepreneurship Training Program for Undergraduates of Beijing Forestry University(Grant No.X202410022268).
文摘Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring,urban planning,and disaster assessment.However,traditional methods exhibit deficiencies in detail recovery and noise suppression,particularly when processing complex landscapes(e.g.,forests,farmlands),leading to artifacts and spectral distortions that limit practical utility.To address this,we propose an enhanced Super-Resolution Generative Adversarial Network(SRGAN)framework featuring three key innovations:(1)Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing;(2)A multi-loss joint optimization strategy dynamically weighting Charbonnier loss(β=0.5),Visual Geometry Group(VGG)perceptual loss(α=1),and adversarial loss(γ=0.1)to synergize pixel-level accuracy and perceptual quality;(3)A multi-scale residual network(MSRN)capturing cross-scale texture features(e.g.,forest canopies,mountain contours).Validated on Sentinel-2(10 m)and SPOT-6/7(2.5 m)datasets covering 904 km2 in Motuo County,Xizang,our method outperforms the SRGAN baseline(SR4RS)with Peak Signal-to-Noise Ratio(PSNR)gains of 0.29 dB and Structural Similarity Index(SSIM)improvements of 3.08%on forest imagery.Visual comparisons confirm enhanced texture continuity despite marginal Learned Perceptual Image Patch Similarity(LPIPS)increases.The method significantly improves noise robustness and edge retention in complex geomorphology,demonstrating 18%faster response in forest fire early warning and providing high-resolution support for agricultural/urban monitoring.Future work will integrate spectral constraints and lightweight architectures.
文摘Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.
基金supported by the National Natural Science Foundation of China(72071106)Jiangsu Provincial Social Science Fund(23EYA001)+1 种基金Jiangsu Provincial Education Science Planning Fund(Ba/2024/08)Jiangsu Higher Education Association Fund(24FYHLX090)。
文摘A performance improvement model of research and development(R&D)institutions based on evolutionary game and Bayesian network is proposed.First,the nature and performance factors of new R&D institutions are systematically analyzed,the appropriate factor model is found,and the sharing of performance benefits between institutions and employees,the change in distribution proportion,and the risk of institutional improvement and employee cooperation are considered.Second,based on the mechanism improvement and employee cooperation,the payment matrix is given and evolutionary game analysis is carried out to obtain a stable and balanced institutional improvement probability and employee cooperation probability.These two probability values are substituted into the Bayesian network model of performance improvement of new R&D institutions,and the posterior probability of performance improvement is predicted by Bayesian network reasoning and diagnosis to find effective improvement measures.Finally,practical case analysis is given to verify the effectiveness and practicability of the proposed method.
基金supported by the National Natural Science Foundation of China(Nos.12247126 and 12375123)Henan Postdoctoral Foundation(No.HN2024013)the Natural Science Foundation of Henan Province(No.242300421048)。
文摘Research on neutron-induced fission product yields of^(232)Th is crucial for understanding the competition between symmetric and asymmetric fission in actinide nuclei.However,obtaining complete isotopic yield distributions over a wide range of neutron energies remains a challenge.In this study,a Bayesian neural network model was developed to predict the independent(IND)and cumulative fission yields of^(232)Th under neutron irradiation at various incident energies.To address the limited availability of experimental data for the analysis of IND mass distributions,we substituted mass-number-based yields with the yields of specific isotopes.Furthermore,physical phenomena or quantities,such as the odd-even effect and isospin,were introduced as constraints to enhance the physical consistency of the predictions.The impact of these constraints was evaluated using mass-chain yield distributions and their dependence on energy.Incorporating physical constraints significantly improves the prediction accuracy,yielding more reliable and physically meaningful fission yield data for nuclear physics and reactor design applications.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.52271317 and 52071149)the Fundamental Research Funds for the Central Universities(HUST:2019kfy XJJS007)。
文摘To address the problems of low accuracy by the CONWEP model and poor efficiency by the Coupled Eulerian-Lagrangian(CEL)method in predicting close-range air blast loads of cylindrical charges,a neural network-based simulation(NNS)method with higher accuracy and better efficiency was proposed.The NNS method consisted of three main steps.First,the parameters of blast loads,including the peak pressures and impulses of cylindrical charges with different aspect ratios(L/D)at different stand-off distances and incident angles were obtained by two-dimensional numerical simulations.Subsequently,incident shape factors of cylindrical charges with arbitrary aspect ratios were predicted by a neural network.Finally,reflected shape factors were derived and implemented into the subroutine of the ABAQUS code to modify the CONWEP model,including modifications of impulse and overpressure.The reliability of the proposed NNS method was verified by related experimental results.Remarkable accuracy improvement was acquired by the proposed NNS method compared with the unmodified CONWEP model.Moreover,huge efficiency superiority was obtained by the proposed NNS method compared with the CEL method.The proposed NNS method showed good accuracy when the scaled distance was greater than 0.2 m/kg^(1/3).It should be noted that there is no need to generate a new dataset again since the blast loads satisfy the similarity law,and the proposed NNS method can be directly used to simulate the blast loads generated by different cylindrical charges.The proposed NNS method with high efficiency and accuracy can be used as an effective method to analyze the dynamic response of structures under blast loads,and it has significant application prospects in designing protective structures.
基金supported by National Natural Science Foundation of China(Grant Nos.52074312 and 52211530097)CNPC Science and Technology Innovation Foundation(Grant No.2021DQ02-0505).
文摘Wellbore breakout is one of the critical issues in drilling due to the fact that the related problems result in additional costs and impact the drilling scheme severely.However,the majority of such wellbore breakout analyses were based on continuum mechanics.In addition to failure in intact rocks,wellbore breakouts can also be initiated along natural discontinuities,e.g.weak planes and fractures.Furthermore,the conventional models in wellbore breakouts with uniform distribution fractures could not reflect the real drilling situation.This paper presents a fully coupled hydro-mechanical model of the SB-X well in the Tarim Basin,China for evaluating wellbore breakouts in heavily fractured rocks under anisotropic stress states using the distinct element method(DEM)and the discrete fracture network(DFN).The developed model was validated against caliper log measurement,and its stability study was carried out by stress and displacement analyses.A parametric study was performed to investigate the effects of the characteristics of fracture distribution(orientation and length)on borehole stability by sensitivity studies.Simulation results demonstrate that the increase of the standard deviation of orientation when the fracture direction aligns parallel or perpendicular to the principal stress direction aggravates borehole instability.Moreover,an elevation in the average fracture length causes the borehole failure to change from the direction of the minimum in-situ horizontal principal stress(i.e.the direction of wellbore breakouts)towards alternative directions,ultimately leading to the whole wellbore failure.These findings provide theoretical insights for predicting wellbore breakouts in heavily fractured rocks.
基金supported by the National Key Research and Development Program of China No.2023YFA1009500.
文摘With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.
基金supported by National Natural Science Foundation of China(62473341)Key Research and Development Special Project of Henan Province(221111210500)Key Research and Development Special Project of Henan Province(242102211071,242102210142,232102211053).
文摘The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic,highlighting the growing importance of network security.Intrusion Detection Systems(IDS)are essential for safeguarding network integrity.To address the low accuracy of existing intrusion detection models in identifying network attacks,this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network(SA-ResNet).Utilizing residual connections can effectively capture local features in the data;by introducing a spatial attention mechanism,the global dependency relationships of intrusion features can be extracted,enhancing the intrusion recognition model’s focus on the global features of intrusions,and effectively improving the accuracy of intrusion recognition.The proposed model in this paper was experimentally verified on theNSL-KDD dataset.The experimental results showthat the intrusion recognition accuracy of the intrusion detection method based on SA-ResNet has reached 99.86%,and its overall accuracy is 0.41% higher than that of traditional Convolutional Neural Network(CNN)models.
基金supported in part by National Natural Science Foundation of China under Grant 52007026.
文摘With the widespread implementation of distributed generation(DG)and the integration of soft open point(SOP)into the distribution network(DN),the latter is steadily transitioning into a flexible distribution network(FDN),the calculation of carbon flow distribution in FDN is more difficult.To this end,this study constructs a model for low-carbon optimal operations within the FDN on the basis of enhanced carbon emission flow(CEF).First,the carbon emission characteristics of FDNs are scrutinized and an improved method for calculating carbon flow within these networks is proposed.Subsequently,a model for optimizing low-carbon operations within FDNs is formulated based on the refined CEF,which merges the specificities of DG and intelligent SOP.Finally,this model is scrutinized using an upgraded IEEE 33-node distribution system,a comparative analysis of the cases reveals that when DG and SOP are operated in a coordinated manner in the FDN,with the cost of electricity generation was reduced by 40.63 percent and the cost of carbon emissions by 10.18 percent.The findings indicate that the judicious optimization of areas exhibiting higher carbon flow rates can effectively enhance the economic efficiency of DN operations and curtail the carbon emissions of the overall network.
基金supported by the National Natural Science Foundation of China(Grant No.42076214)Natural Science Foundation of Shandong Province(Grant No.ZR2024QD057).
文摘Timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas.At present,numerical model forecasting consumes too many resources and takes too long to compute,while neural network forecasting lacks regional data to train regional forecasting models.In this study,we used the DUAL wind model to build typhoon wind fields,and constructed a typhoon database of 75 processes in the northern South China Sea using the coupled Advanced Circulation-Simulating Waves Nearshore(ADCIRC-SWAN)model.Then,a neural network with a Res-U-Net structure was trained using the typhoon database to forecast the typhoon processes in the validation dataset,and an excellent storm surge forecasting effect was achieved in the Pearl River Estuary region.The storm surge forecasting effect of stronger typhoons was improved by adding a branch structure and transfer learning.
基金supported by the National Natural Science Founion of China(U2241285).
文摘Accurate and efficient prediction of the distribution of surface loads on buildings subjected to explosive effects is crucial for rapidly calculating structural dynamic responses,establishing effective protective measures,and designing civil defense engineering solutions.Current state-of-the-art methods face several issues:Experimental research is difficult and costly to implement,theoretical research is limited to simple geometries and lacks precision,and direct simulations require substantial computational resources.To address these challenges,this paper presents a data-driven method for predicting blast loads on building surfaces.This approach increases both the accuracy and computational efficiency of load predictions when the geometry of the building changes while the explosive yield remains constant,significantly improving its applicability in complex scenarios.This study introduces an innovative encoder-decoder graph neural network model named BlastGraphNet,which uses a message-passing mechanism to predict the overpressure and impulse load distributions on buildings with conventional and complex geometries during explosive events.The model also facilitates related downstream applications,such as damage mode identification and rapid assessment of virtual city explosions.The calculation results indicate that the prediction error of the model for conventional building tests is less than 2%,and its inference speed is 3-4 orders of magnitude faster than that of state-of-the-art numerical methods.In extreme test cases involving buildings with complex geometries and building clusters,the method achieved high accuracy and excellent generalizability.The strong adaptability and generalizability of BlastGraphNet confirm that this novel method enables precise real-time prediction of blast loads and provides a new paradigm for damage assessment in protective engineering.
基金supported by the National Natural Science Foundation of China(Grant No.92152301)the National Key Research and Development Program of China(Grant No.2022YFB4300200)the Shaanxi Provincial Key Research and Development Program(Grant No.2023-ZDLGY-27).
文摘Physics-informed neural networks(PINNs)have shown remarkable prospects in solving the forward and inverse problems involving partial differential equations(PDEs).The method embeds PDEs into the neural network by calculating the PDE loss at a set of collocation points,providing advantages such as meshfree and more convenient adaptive sampling.However,when solving PDEs using nonuniform collocation points,PINNs still face challenge regarding inefficient convergence of PDE residuals or even failure.In this work,we first analyze the ill-conditioning of the PDE loss in PINNs under nonuniform collocation points.To address the issue,we define volume weighting residual and propose volume weighting physics-informed neural networks(VW-PINNs).Through weighting the PDE residuals by the volume that the collocation points occupy within the computational domain,we embed explicitly the distribution characteristics of collocation points in the loss evaluation.The fast and sufficient convergence of the PDE residuals for the problems involving nonuniform collocation points is guaranteed.Considering the meshfree characteristics of VW-PINNs,we also develop a volume approximation algorithm based on kernel density estimation to calculate the volume of the collocation points.We validate the universality of VW-PINNs by solving the forward problems involving flow over a circular cylinder and flow over the NACA0012 airfoil under different inflow conditions,where conventional PINNs fail.By solving the Burgers’equation,we verify that VW-PINNs can enhance the efficiency of existing the adaptive sampling method in solving the forward problem by three times,and can reduce the relative L 2 error of conventional PINNs in solving the inverse problem by more than one order of magnitude.
基金Supported by National Natural Science Foundation of China(Grant Nos.52222508 and 52335011)。
文摘Heat dissipation performance is critical to the design of high-end equipment,such as integrated chips and high-precision machine tools.Owing to the advantages of artificial intelligence in solving complex tasks involving a large number of variables,researchers have exploited deep learning to expedite the optimization of material properties,such as the heat dissipation of solid isotropic materials with penalization(SIMP).However,because the approach is limited by discrete datasets and labeled training forms,ensuring the continuous adaptation of the condition domain and maintaining the stability of the design structure remain major challenges in the current intelligent design methodology for thermally conductive structures.In this study,we propose an innovative intelligent design fram-ework integrating Conditional Deep Convolutional Generative Adversarial Networks(CDCGAN)with SIMP,capable of creating topology structures that meet prescribed thermal conduction performance.This proposed design strategy significantly reduces the computational time required to solve symmetric and random heat sink problems compared with existing design approaches and is approximately 98%faster than standard SIMP methods and 55.5%faster than conventional deep-learning-based methods.In addition,we benchmarked the design performance of the proposed framework against theoretical structural designs via experimental measurements.We observed a 50.1%reduction in the average temperature and a 28.2%reduction in the highest temperature in our designed topology compared with those theoretical structure designs.
文摘The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system,the connection point of each network is regarded as a neuron in the nervous system,and each connection point has input and output functions.Only when the input of nodes reaches a certain threshold can the output function of nodes be stimulated.Using the networking mode of the artificial neural network model,the mobile node can output in multiple directions.If the input direction of a certain path is the same as that of other nodes,it can choose to avoid and choose another path.The weighted value of each path between nodes is different,which means that the influence of the front node on the current node varies.The control method based on the artificial neural network model can be applied to vehicle control,household sweeping robots,and other fields,and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.
