The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combi...The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.展开更多
Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face...Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face challenges,including high metal usage,high process costs,and low cyclohexene yield.This study utilizes existing literature data combined with machine learning methods to analyze the factors influencing benzene conversion,cyclohexene selectivity,and yield in the benzene hydrogenation to cyclohexene reaction.It constructs predictive models based on XGBoost and Random Forest algorithms.After analysis,it was found that reaction time,Ru content,and space velocity are key factors influencing cyclohexene yield,selectivity,and benzene conversion.Shapley Additive Explanations(SHAP)analysis and feature importance analysis further revealed the contribution of each variable to the reaction outcomes.Additionally,we randomly generated one million variable combinations using the Dirichlet distribution to attempt to predict high-yield catalyst formulations.This paper provides new insights into the application of machine learning in heterogeneous catalysis and offers some reference for further research.展开更多
Large visual language models such as CLIP have demonstrated impressive performance on various downstream tasks involving natural images,by leveraging prompt learning.However,these models often falter when applied to t...Large visual language models such as CLIP have demonstrated impressive performance on various downstream tasks involving natural images,by leveraging prompt learning.However,these models often falter when applied to tasks involving medical images.We provide an experimental insight into this phenomenon:CLIP is insensitive to the class names of medical images.For instance,replacing the class name“medulloblastoma”(a type of brain tumor)with“dog”in prompts has minimal impact on performance,a phenomenon not observed with natural images.To realign prompt learning with medical image recognition,we propose a novel prompt learning strategy,termed prompt reverse learning(PeLen).Different from the existing methods that adapt CLIP’s representations to downstream tasks,PeLen adapts task-specific representations to CLIP’s representations.Built upon the insensitivity to the class names of medical images,PeLen designates natural images and their class names to represent a specific class of medical images and class names,e.g.,allowing the image and text of a dog to correspond to the image and text of medulloblastoma.Consequently,PeLen learns prompts to align the representations between the medical images and the visual and textual representations of natural images.Our experiments demonstrate the efficacy of PeLen for medical image recognition.展开更多
Decentralized federated learning(DFL)has evolved as a favored paradigm for cultivating machine learning models on extensive data in edge computing,thanks to its prowess in circumventing potential bottlenecks inherent ...Decentralized federated learning(DFL)has evolved as a favored paradigm for cultivating machine learning models on extensive data in edge computing,thanks to its prowess in circumventing potential bottlenecks inherent in conventional parameter server architectures.However,existing DFL solutions predominantly leverage deterministic topologies,contending with system heterogeneity and non-independent and identically distributed(non-IID)local datasets.This dilemma often escalates bandwidth costs and extends convergence rates within fluctuating networks.To this end,we present a groundbreaking mechanism named data-efficient decentralized federated learning(DE-DFL),specifically designed to accelerate the model training process.In DE-DFL,each client interacts with its neighbors,e.g.,model exchange,according to an approximate policy at every round,so as to reduce bandwidth consumption.Subsequently,we then propose an experience-driven algorithm to adaptively determine the optimal communication policy for all clients according to real-time system situations(e.g.,data distribution and bandwidth resource).Our innovative mechanism has been rigorously validated against standard models and datasets,thereby corroborating its superior efficacy.The experimental results reveal that DE-DFL significantly reduces the model training completion time by approximately 68.7% and enhances test accuracy by 6.9%u nder bandwidth constraints when compared to existing state-of-the-art solutions.展开更多
The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))an...The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))and Faradaic efficiency(F_(NH3)).However,conventional experimental methods for screening high-activity NORR catalysts often entail high resource consumption and time costs.Machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model that integrates multiple algorithms,to allow for a systematic investigation of the key descriptors governing NORR performance based on an experimental dataset.Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R^(2)of 0.9223 and an RMSE of 0.0608 for predicting on the test set,whereas the Stacked-RF model achieved an R^(2)of 0.9042 and an RMSE of 0.0900 for predicting.The stacked ensemble model integrates the strengths of individual algorithms and demonstrates strong NORR prediction performance while avoiding overfitting.SHAP feature analysis results revealed that the Cu content in the catalyst composition has the most significant impact on catalytic performance.Moreover,the combination of the wet chemical reduction synthesis,a carbon fiber(CF)conductive substrate,and HCl electrolyte is more favorable for enhancing catalytic activity.Additionally,moderately lowering the working potential,controlling the electrolyte volume at low to medium levels,reducing catalyst loading,and increasing electrolyte concentration were found to synergistically enhance both and.展开更多
Nonlinear classification models are widely used in various fields due to their excellent performance in handling complex problems.This paper investigates the learning performance of nonlinear classification models bas...Nonlinear classification models are widely used in various fields due to their excellent performance in handling complex problems.This paper investigates the learning performance of nonlinear classification models based on Markov sampling,which builds upon the traditional framework using i.i.d.samples.Subsequently,we introduce a ueMC-NL algorithm,tailored specifically for nonlinear classification models,facilitating the production of ueMC samples from a finite dataset.Numerical investigations on the random forest and the MLP model reveal that nonlinear classification models utilizing ueMC samples yield lower misclassification rates compared to i.i.d.samples.展开更多
The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for e...The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for evaluating the efficacy of WGPC.Unlike conventional testing methods,machine learning techniques offer precise and reliable predictions of concrete’s compressive strength,especially in its long-term mechanical properties.In this work,four models,namely Multiple Linear Regression(MLR),Back Propagation Neural Network(BPNN),Support Vector Regression(SVR),and Random Forest Regression(RFR)were employed.Furthermore,particle swarm optimization(PSO)algorithm and cross-validation techniques were applied to fine-tune the model parameters,striving for peak prediction performance.The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts.Notably,the PSO-RFR model excels among all evaluated models,showcasing superior performance on the testing dataset.It achieves a coefficient of determination(R^(2))of 0.9231,a mean absolute error(MAE)of 2.1073,and a root mean square error(RMSE)of 3.6903.When compared to experimental results,the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy.Notably,the PSO-BPNN model exhibits the closest R^(2)values between its training and test sets.This close alignment of R^(2)values between the training and testing sets reflects the PSO-BPNN model’s superior generalization ability for unseen data.The findings present an efficient method for predicting concrete’s compressive strength,contributing to the sustainable development of concrete materials,and providing theoretical support for their research and application.展开更多
Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that empl...Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.展开更多
With the increasing maturity of multi-UAV technology and its broad applications in scenarios such as UAV roundup tasks,this paper proposes a novel approach to enhance interception efficiency and system robustness by a...With the increasing maturity of multi-UAV technology and its broad applications in scenarios such as UAV roundup tasks,this paper proposes a novel approach to enhance interception efficiency and system robustness by addressing insufficient historical data utilization and inadequate environmental explo-ration.The multi-UAV roundup problem is formulated as a Markov Decision Process(MDP),and an Improved Cross-Entropy Method with Intrinsic Curiosity-enhanced Multi-Agent Twin Delayed Deep Deterministic Policy Gradient(I2C-MATD3)is designed.Specifically,an Improved Cross-Entropy Method(ICEM)based on global elite samples rapidly optimizes training strategies while generating extensive experience for a Multi-Agent Twin Delayed Deep Deterministic Policy Gradient algorithm augmented with intrinsic curiosity rewards(IC-MATD3).In turn,IC-MATD3 guides the optimization direction of ICEM,enabling a synergistic interaction that facilitates effective historical data exploitation and pro-active environmental exploration for UAV agents to accomplish roundup tasks.Experiments in complex scenarios demonstrate that the proposed algorithm achieves superior training efficiency and conver-gence performance compared to state-of-the-art multi-agent reinforcement learning(MARL)methods.Robustness tests and ablation experiments further validate its enhanced generalizability and robustness.展开更多
Deep reinforcement learning algorithms are revolutionizing intelligent decision-making in air combat,drawing widespread attention and extensive research.However,air combat agents trained with these algorithms face sig...Deep reinforcement learning algorithms are revolutionizing intelligent decision-making in air combat,drawing widespread attention and extensive research.However,air combat agents trained with these algorithms face significant challenges,such as limited decision-making capacities due to adversarial training against relatively fixed and singular expert strategies,and a lack of interpretability and reliability in their decisionmaking processes.To tackle these issues,this paper proposes a self-play training mechanism based on policy switching and opponent selection,allowing air combat agents to refine their capabilities via engaging with previous versions of themselves.Additionally,an explainable decision tree model is developed to clarify the decision logic of these agents.Simulations and results demonstrate that the proposed self-play training approach significantly enhances the decision-making abilities of air combat agents,with late-stage agents showing a 38%improvement over early-stage agents in confrontations with an expert strategy.Moreover,the explainable decision tree model effectively elucidates the decision logic and achieves an 86%win rate against the expert strategy,comparable to the 88%win rate of the air combat agents.展开更多
As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,t...As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.展开更多
[Objective]There are several critical challenges in automated safflower harvesting,particularly the inefficiencies in path planning,suboptimal route quality,and limited decision-making capability under dynamic and com...[Objective]There are several critical challenges in automated safflower harvesting,particularly the inefficiencies in path planning,suboptimal route quality,and limited decision-making capability under dynamic and complex environments.To solve these issues,the problem was formulated as a three-dimensional traveling salesman problem and an enhanced reinforcement learning model named actor-critic reinforcement learning pointer network(AC-RL-PtrNet)was proposed,specifically designed for deployment on intelligent safflower picking robots in agricultural settings.[Methods]First,to address the inherent limitations of conventional attention mechanisms in dynamic environments with complex spatial structures,an enhanced attention module was proposed based on the dynamic exponential moving average framework.By combining multi-head attention,spatial distance encoding,and adaptive exponential smoothing,the improved design allowed the model to better capture long-range dependencies and spatial context among safflowers.Meanwhile,to minimize computational cost while preserving inference quality,a structured pruning approach was adopted,which selectively removed redundant connections in the long short-term memory gates and fully connected layers.In parallel,the critic network was redesigned to improve learning stability and accuracy.This was achieved through the inclusion of batch normalization,residual feature aggregation,and a multi-layer value estimation head,all of which contributed to a tighter actorcritic synergy during policy training.[Results and Discussions]To quantitatively assess the impact of each component,ablation experiments were conducted across various configurations.The results confirmed that each module contributed distinct benefits,while their combination yielded the highest improvements in both planning precision and inference efficiency.This coordinated actor-critic design effectively enhanced both trajectory quality and decision stability,which were critical in sequential robotic picking tasks.Experimental results also demonstrated that,compared with traditional swarm intelligence algorithms particle swarm optimization(PSO),ant colony optimization(ACO),and non-dominated sorting genetic algorithm,the proposed AC-RL-PtrNet model achieved a planning time improvement ranging from-2.63%to 61.87%on the 25-target dataset and from 22.93%to 59.1%on the 31-target dataset.Meanwhile,the optimized paths were significantly shortened across different planning instances,indicating robust generalization capability under varied problem scales.Furthermore,field experiments provided concrete validation of the model's practical applicability.When deployed on a mobile picking robot in real safflower fields,the AC-RL-PtrNet achieved a 9.56%reduction in path length and 5.43%time saved for a 25-target picking task,and a 20.17%path reduction and 29.70%time saving for a 31-target scenario involving a different safflower variety.Overall,these results all indicated that the proposed method exhibited significant advantages in enhancing path planning efficiency and optimizing path quality.[Conclusions]This study offers a practical solution for achieving efficient and robust automatic picking by safflower picking robots and provides new insights into solving 3D combinatorial optimization problems.展开更多
Investigating the detonation reaction zone structures of high explosives is significant for understanding detonation reaction mechanism.This study employed an integrated approach combining machine learning prediction,...Investigating the detonation reaction zone structures of high explosives is significant for understanding detonation reaction mechanism.This study employed an integrated approach combining machine learning prediction,theoretical calculation,and experimental characterization to determine the detonation reaction zone width of CL-20-based aluminized explosive.In this study,the detonation reaction zone refers to the reaction zone between the von Neumann(VN)peak and sonic point,which usually means the so-called detonation driving zone(DDZ).For the machine learning prediction,an ensemble model integrating Random Forest and Support Vector Regression was developed to predict the reaction zone width using a dataset of 19 publicly available samples.For the theoretical calculation,the Wood-Kirkwood(W-K)detonation theory model was utilized to implement numerical calculation of the reaction zone structures,incorporating chemical reaction kinetics to describe the detonation reaction progress.In experimental characterization,the Photon Doppler Velocimetry(PDV)was applied with LiF as the optical window to measure the particle velocity profile of detonation products and derive the reaction zone width.The results indicate that the reaction zone width values are 0.25 mm,0.28 mm,and 0.26 mm obtained from machine learning prediction,theoretical calculation,and experimental characterization,respectively.The corresponding velocities at the Chapman-Jouguet(CJ)point are 1,938 m/s,2,047 m/s,and 1,982 m/s,respectively.The maximum relative deviation in reaction zone width among three methods is approximately 7.7%,while that for CJ particle velocity is approximately 3.3%.These results from all three methods agree well within engineering error.This validates the effectiveness of integrating machine learning prediction,theoretical calculation and advanced experimental techniques for studying the detonation reaction zone structures of high explosives.This research provides insights into the detonation reaction mechanism and reaction zone characteristics of CL-20-based aluminized explosive.展开更多
基金Guangzhou Metro Scientific Research Project(No.JT204-100111-23001)Chongqing Municipal Special Project for Technological Innovation and Application Development(No.CSTB2022TIAD-KPX0101)Science and Technology Research and Development Program of China State Railway Group Co.,Ltd.(No.N2023G045)。
文摘The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability.However,research on uplift resistance concerning special-shaped shield tunnels is limited.This study combines numerical simulation with machine learning techniques to explore this issue.It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient.Through the finite element software,Plaxis3D,the study simulates six key parameters—shape coefficient,burial depth ratio,tunnel’s longest horizontal length,internal friction angle,cohesion,and soil submerged bulk density—that impact uplift resistance across different conditions.Employing XGBoost and ANN methods,the feature importance of each parameter was analyzed based on the numerical simulation results.The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil,whereas other parameters exhibit the contrary effects.Furthermore,the study reveals a diminishing trend in the feature importance of buried depth ratio,internal friction angle,tunnel longest horizontal length,cohesion,soil submerged bulk density,and shape coefficient in influencing uplift resistance.
基金Supported by CAS Basic and Interdisciplinary Frontier Scientific Research Pilot Project(XDB1190300,XDB1190302)Youth Innovation Promotion Association CAS(Y2021056)+1 种基金Joint Fund of the Yulin University and the Dalian National Laboratory for Clean Energy(YLU-DNL Fund 2022007)The special fund for Science and Technology Innovation Teams of Shanxi Province(202304051001007)。
文摘Cyclohexene is an important raw material in the production of nylon.Selective hydrogenation of benzene is a key method for preparing cyclohexene.However,the Ru catalysts used in current industrial processes still face challenges,including high metal usage,high process costs,and low cyclohexene yield.This study utilizes existing literature data combined with machine learning methods to analyze the factors influencing benzene conversion,cyclohexene selectivity,and yield in the benzene hydrogenation to cyclohexene reaction.It constructs predictive models based on XGBoost and Random Forest algorithms.After analysis,it was found that reaction time,Ru content,and space velocity are key factors influencing cyclohexene yield,selectivity,and benzene conversion.Shapley Additive Explanations(SHAP)analysis and feature importance analysis further revealed the contribution of each variable to the reaction outcomes.Additionally,we randomly generated one million variable combinations using the Dirichlet distribution to attempt to predict high-yield catalyst formulations.This paper provides new insights into the application of machine learning in heterogeneous catalysis and offers some reference for further research.
基金supported by the National Natural Science Foundation of China(62222117).
文摘Large visual language models such as CLIP have demonstrated impressive performance on various downstream tasks involving natural images,by leveraging prompt learning.However,these models often falter when applied to tasks involving medical images.We provide an experimental insight into this phenomenon:CLIP is insensitive to the class names of medical images.For instance,replacing the class name“medulloblastoma”(a type of brain tumor)with“dog”in prompts has minimal impact on performance,a phenomenon not observed with natural images.To realign prompt learning with medical image recognition,we propose a novel prompt learning strategy,termed prompt reverse learning(PeLen).Different from the existing methods that adapt CLIP’s representations to downstream tasks,PeLen adapts task-specific representations to CLIP’s representations.Built upon the insensitivity to the class names of medical images,PeLen designates natural images and their class names to represent a specific class of medical images and class names,e.g.,allowing the image and text of a dog to correspond to the image and text of medulloblastoma.Consequently,PeLen learns prompts to align the representations between the medical images and the visual and textual representations of natural images.Our experiments demonstrate the efficacy of PeLen for medical image recognition.
基金supported by the National Key Research and Development Program of China(2021YFB3301500)Fundamental Research Funds for the Central Universities(WK2150110033,WK2150110030).
文摘Decentralized federated learning(DFL)has evolved as a favored paradigm for cultivating machine learning models on extensive data in edge computing,thanks to its prowess in circumventing potential bottlenecks inherent in conventional parameter server architectures.However,existing DFL solutions predominantly leverage deterministic topologies,contending with system heterogeneity and non-independent and identically distributed(non-IID)local datasets.This dilemma often escalates bandwidth costs and extends convergence rates within fluctuating networks.To this end,we present a groundbreaking mechanism named data-efficient decentralized federated learning(DE-DFL),specifically designed to accelerate the model training process.In DE-DFL,each client interacts with its neighbors,e.g.,model exchange,according to an approximate policy at every round,so as to reduce bandwidth consumption.Subsequently,we then propose an experience-driven algorithm to adaptively determine the optimal communication policy for all clients according to real-time system situations(e.g.,data distribution and bandwidth resource).Our innovative mechanism has been rigorously validated against standard models and datasets,thereby corroborating its superior efficacy.The experimental results reveal that DE-DFL significantly reduces the model training completion time by approximately 68.7% and enhances test accuracy by 6.9%u nder bandwidth constraints when compared to existing state-of-the-art solutions.
基金Supported by Doctoral Project of Natural Science Foundation of Liaoning Province(2025-BS-0373).
文摘The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))and Faradaic efficiency(F_(NH3)).However,conventional experimental methods for screening high-activity NORR catalysts often entail high resource consumption and time costs.Machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model that integrates multiple algorithms,to allow for a systematic investigation of the key descriptors governing NORR performance based on an experimental dataset.Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R^(2)of 0.9223 and an RMSE of 0.0608 for predicting on the test set,whereas the Stacked-RF model achieved an R^(2)of 0.9042 and an RMSE of 0.0900 for predicting.The stacked ensemble model integrates the strengths of individual algorithms and demonstrates strong NORR prediction performance while avoiding overfitting.SHAP feature analysis results revealed that the Cu content in the catalyst composition has the most significant impact on catalytic performance.Moreover,the combination of the wet chemical reduction synthesis,a carbon fiber(CF)conductive substrate,and HCl electrolyte is more favorable for enhancing catalytic activity.Additionally,moderately lowering the working potential,controlling the electrolyte volume at low to medium levels,reducing catalyst loading,and increasing electrolyte concentration were found to synergistically enhance both and.
文摘Nonlinear classification models are widely used in various fields due to their excellent performance in handling complex problems.This paper investigates the learning performance of nonlinear classification models based on Markov sampling,which builds upon the traditional framework using i.i.d.samples.Subsequently,we introduce a ueMC-NL algorithm,tailored specifically for nonlinear classification models,facilitating the production of ueMC samples from a finite dataset.Numerical investigations on the random forest and the MLP model reveal that nonlinear classification models utilizing ueMC samples yield lower misclassification rates compared to i.i.d.samples.
文摘The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for evaluating the efficacy of WGPC.Unlike conventional testing methods,machine learning techniques offer precise and reliable predictions of concrete’s compressive strength,especially in its long-term mechanical properties.In this work,four models,namely Multiple Linear Regression(MLR),Back Propagation Neural Network(BPNN),Support Vector Regression(SVR),and Random Forest Regression(RFR)were employed.Furthermore,particle swarm optimization(PSO)algorithm and cross-validation techniques were applied to fine-tune the model parameters,striving for peak prediction performance.The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts.Notably,the PSO-RFR model excels among all evaluated models,showcasing superior performance on the testing dataset.It achieves a coefficient of determination(R^(2))of 0.9231,a mean absolute error(MAE)of 2.1073,and a root mean square error(RMSE)of 3.6903.When compared to experimental results,the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy.Notably,the PSO-BPNN model exhibits the closest R^(2)values between its training and test sets.This close alignment of R^(2)values between the training and testing sets reflects the PSO-BPNN model’s superior generalization ability for unseen data.The findings present an efficient method for predicting concrete’s compressive strength,contributing to the sustainable development of concrete materials,and providing theoretical support for their research and application.
基金Project(42077244)supported by the National Natural Science Foundation of ChinaProject(2020-05)supported by the Open Research Fund of Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization,China。
文摘Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.
基金the National Key Lab-oratory of Air-based Information Perception and Fusion(Grant No.202510)the Key Research and Development Program of Shaanxi Province(Grant No.2023-GHZD-33)+2 种基金the Fundamental Research Funds for the Central Universities(Grant No.H20250607)the Open Project of the State Key Laboratory of Intelligent Game(Grant No.ZBKF-23-05)the National Nature Science Foundation of China(Grant No.62003267)to provide fund for conducting experiments.
文摘With the increasing maturity of multi-UAV technology and its broad applications in scenarios such as UAV roundup tasks,this paper proposes a novel approach to enhance interception efficiency and system robustness by addressing insufficient historical data utilization and inadequate environmental explo-ration.The multi-UAV roundup problem is formulated as a Markov Decision Process(MDP),and an Improved Cross-Entropy Method with Intrinsic Curiosity-enhanced Multi-Agent Twin Delayed Deep Deterministic Policy Gradient(I2C-MATD3)is designed.Specifically,an Improved Cross-Entropy Method(ICEM)based on global elite samples rapidly optimizes training strategies while generating extensive experience for a Multi-Agent Twin Delayed Deep Deterministic Policy Gradient algorithm augmented with intrinsic curiosity rewards(IC-MATD3).In turn,IC-MATD3 guides the optimization direction of ICEM,enabling a synergistic interaction that facilitates effective historical data exploitation and pro-active environmental exploration for UAV agents to accomplish roundup tasks.Experiments in complex scenarios demonstrate that the proposed algorithm achieves superior training efficiency and conver-gence performance compared to state-of-the-art multi-agent reinforcement learning(MARL)methods.Robustness tests and ablation experiments further validate its enhanced generalizability and robustness.
基金supported by the Joint Funds of the National Natural Science Foundation of China(U2341216).
文摘Deep reinforcement learning algorithms are revolutionizing intelligent decision-making in air combat,drawing widespread attention and extensive research.However,air combat agents trained with these algorithms face significant challenges,such as limited decision-making capacities due to adversarial training against relatively fixed and singular expert strategies,and a lack of interpretability and reliability in their decisionmaking processes.To tackle these issues,this paper proposes a self-play training mechanism based on policy switching and opponent selection,allowing air combat agents to refine their capabilities via engaging with previous versions of themselves.Additionally,an explainable decision tree model is developed to clarify the decision logic of these agents.Simulations and results demonstrate that the proposed self-play training approach significantly enhances the decision-making abilities of air combat agents,with late-stage agents showing a 38%improvement over early-stage agents in confrontations with an expert strategy.Moreover,the explainable decision tree model effectively elucidates the decision logic and achieves an 86%win rate against the expert strategy,comparable to the 88%win rate of the air combat agents.
基金supported by the National Key Research and Development Program of China(No.2022YFB4300902).
文摘As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.
基金Natural Science Foundation of Xinjiang Uygur Autonomous Region,China Under Grant(2023D01C190)National Science and Technology Major Project(2022ZD0115801)。
文摘[Objective]There are several critical challenges in automated safflower harvesting,particularly the inefficiencies in path planning,suboptimal route quality,and limited decision-making capability under dynamic and complex environments.To solve these issues,the problem was formulated as a three-dimensional traveling salesman problem and an enhanced reinforcement learning model named actor-critic reinforcement learning pointer network(AC-RL-PtrNet)was proposed,specifically designed for deployment on intelligent safflower picking robots in agricultural settings.[Methods]First,to address the inherent limitations of conventional attention mechanisms in dynamic environments with complex spatial structures,an enhanced attention module was proposed based on the dynamic exponential moving average framework.By combining multi-head attention,spatial distance encoding,and adaptive exponential smoothing,the improved design allowed the model to better capture long-range dependencies and spatial context among safflowers.Meanwhile,to minimize computational cost while preserving inference quality,a structured pruning approach was adopted,which selectively removed redundant connections in the long short-term memory gates and fully connected layers.In parallel,the critic network was redesigned to improve learning stability and accuracy.This was achieved through the inclusion of batch normalization,residual feature aggregation,and a multi-layer value estimation head,all of which contributed to a tighter actorcritic synergy during policy training.[Results and Discussions]To quantitatively assess the impact of each component,ablation experiments were conducted across various configurations.The results confirmed that each module contributed distinct benefits,while their combination yielded the highest improvements in both planning precision and inference efficiency.This coordinated actor-critic design effectively enhanced both trajectory quality and decision stability,which were critical in sequential robotic picking tasks.Experimental results also demonstrated that,compared with traditional swarm intelligence algorithms particle swarm optimization(PSO),ant colony optimization(ACO),and non-dominated sorting genetic algorithm,the proposed AC-RL-PtrNet model achieved a planning time improvement ranging from-2.63%to 61.87%on the 25-target dataset and from 22.93%to 59.1%on the 31-target dataset.Meanwhile,the optimized paths were significantly shortened across different planning instances,indicating robust generalization capability under varied problem scales.Furthermore,field experiments provided concrete validation of the model's practical applicability.When deployed on a mobile picking robot in real safflower fields,the AC-RL-PtrNet achieved a 9.56%reduction in path length and 5.43%time saved for a 25-target picking task,and a 20.17%path reduction and 29.70%time saving for a 31-target scenario involving a different safflower variety.Overall,these results all indicated that the proposed method exhibited significant advantages in enhancing path planning efficiency and optimizing path quality.[Conclusions]This study offers a practical solution for achieving efficient and robust automatic picking by safflower picking robots and provides new insights into solving 3D combinatorial optimization problems.
文摘Investigating the detonation reaction zone structures of high explosives is significant for understanding detonation reaction mechanism.This study employed an integrated approach combining machine learning prediction,theoretical calculation,and experimental characterization to determine the detonation reaction zone width of CL-20-based aluminized explosive.In this study,the detonation reaction zone refers to the reaction zone between the von Neumann(VN)peak and sonic point,which usually means the so-called detonation driving zone(DDZ).For the machine learning prediction,an ensemble model integrating Random Forest and Support Vector Regression was developed to predict the reaction zone width using a dataset of 19 publicly available samples.For the theoretical calculation,the Wood-Kirkwood(W-K)detonation theory model was utilized to implement numerical calculation of the reaction zone structures,incorporating chemical reaction kinetics to describe the detonation reaction progress.In experimental characterization,the Photon Doppler Velocimetry(PDV)was applied with LiF as the optical window to measure the particle velocity profile of detonation products and derive the reaction zone width.The results indicate that the reaction zone width values are 0.25 mm,0.28 mm,and 0.26 mm obtained from machine learning prediction,theoretical calculation,and experimental characterization,respectively.The corresponding velocities at the Chapman-Jouguet(CJ)point are 1,938 m/s,2,047 m/s,and 1,982 m/s,respectively.The maximum relative deviation in reaction zone width among three methods is approximately 7.7%,while that for CJ particle velocity is approximately 3.3%.These results from all three methods agree well within engineering error.This validates the effectiveness of integrating machine learning prediction,theoretical calculation and advanced experimental techniques for studying the detonation reaction zone structures of high explosives.This research provides insights into the detonation reaction mechanism and reaction zone characteristics of CL-20-based aluminized explosive.
