With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world tasks.Given the scope of this area,various techniques are found in ...With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world tasks.Given the scope of this area,various techniques are found in the literature.One such notable technique,Multiple Deep Q-Network(DQN)based RL systems use multiple DQN-based-entities,which learn together and communicate with each other.The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed.As more complex DQNs come to the fore,the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training,need for high resources,more training time,and difficulty in fine-tuning leading to performance issues.Taking a cue from the parallel processing found in the nature and its efficacy,we propose a lightweight ensemble based approach for solving the core RL tasks.It uses multiple binary action DQNs having shared state and reward.The benefits of the proposed approach are overall simplicity,faster convergence and better performance compared to conventional DQN based approaches.The approach can potentially be extended to any type of DQN by forming its ensemble.Conducting extensive experimentation,promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks,and Atari 2600 games as compared to recent techniques.The proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task,259.2 on the LunarLander-v2 task,and state-of-the-art results on four out of five Atari 2600 games.展开更多
Federated learning(FL)activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging ...Federated learning(FL)activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging processes.However,in large-scale heterogeneous Internet of Things(IoT)cellular networks,massive multi-dimensional model update iterations and resource-constrained computation are challenging aspects to be tackled significantly.This paper introduces the system model of converging softwaredefined networking(SDN)and network functions virtualization(NFV)to enable device/resource abstractions and provide NFV-enabled edge FL(eFL)aggregation servers for advancing automation and controllability.Multi-agent deep Q-networks(MADQNs)target to enforce a self-learning softwarization,optimize resource allocation policies,and advocate computation offloading decisions.With gathered network conditions and resource states,the proposed agent aims to explore various actions for estimating expected longterm rewards in a particular state observation.In exploration phase,optimal actions for joint resource allocation and offloading decisions in different possible states are obtained by maximum Q-value selections.Action-based virtual network functions(VNF)forwarding graph(VNFFG)is orchestrated to map VNFs towards eFL aggregation server with sufficient communication and computation resources in NFV infrastructure(NFVI).The proposed scheme indicates deficient allocation actions,modifies the VNF backup instances,and reallocates the virtual resource for exploitation phase.Deep neural network(DNN)is used as a value function approximator,and epsilongreedy algorithm balances exploration and exploitation.The scheme primarily considers the criticalities of FL model services and congestion states to optimize long-term policy.Simulation results presented the outperformance of the proposed scheme over reference schemes in terms of Quality of Service(QoS)performance metrics,including packet drop ratio,packet drop counts,packet delivery ratio,delay,and throughput.展开更多
With the advancement of Vehicle-to-Everything(V2X)technology,efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance.Existing methods suffer from h...With the advancement of Vehicle-to-Everything(V2X)technology,efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance.Existing methods suffer from high computational complexity and decision latency under high-density traffic and heterogeneous network conditions.To address these challenges,this study presents an innovative framework that combines Graph Neural Networks(GNNs)with a Double Deep Q-Network(DDQN),utilizing dynamic graph structures and reinforcement learning.An adaptive neighbor sampling mechanism is introduced to dynamically select the most relevant neighbors based on interference levels and network topology,thereby improving decision accuracy and efficiency.Meanwhile,the framework models communication links as nodes and interference relationships as edges,effectively capturing the direct impact of interference on resource allocation while reducing computational complexity and preserving critical interaction information.Employing an aggregation mechanism based on the Graph Attention Network(GAT),it dynamically adjusts the neighbor sampling scope and performs attention-weighted aggregation based on node importance,ensuring more efficient and adaptive resource management.This design ensures reliable Vehicle-to-Vehicle(V2V)communication while maintaining high Vehicle-to-Infrastructure(V2I)throughput.The framework retains the global feature learning capabilities of GNNs and supports distributed network deployment,allowing vehicles to extract low-dimensional graph embeddings from local observations for real-time resource decisions.Experimental results demonstrate that the proposed method significantly reduces computational overhead,mitigates latency,and improves resource utilization efficiency in vehicular networks under complex traffic scenarios.This research not only provides a novel solution to resource allocation challenges in V2X networks but also advances the application of DDQN in intelligent transportation systems,offering substantial theoretical significance and practical value.展开更多
At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown ...At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.展开更多
The rudder mechanism of the X-rudder autonomous underwater cehicle(AUV)is relatively complex,and fault diagnosis capability is an important guarantee for its task execution in complex underwater environments.However,t...The rudder mechanism of the X-rudder autonomous underwater cehicle(AUV)is relatively complex,and fault diagnosis capability is an important guarantee for its task execution in complex underwater environments.However,traditional fault diagnosis methods currently rely on prior knowledge and expert experience,and lack accuracy.In order to improve the autonomy and accuracy of fault diagnosis methods,and overcome the shortcomings of traditional algorithms,this paper proposes an X-steering AUV fault diagnosis model based on the deep reinforcement learning deep Q network(DQN)algorithm,which can learn the relationship between state data and fault types,map raw residual data to corresponding fault patterns,and achieve end-to-end mapping.In addition,to solve the problem of few X-steering fault sample data,Dropout technology is introduced during the model training phase to improve the performance of the DQN algorithm.Experimental results show that the proposed model has improved the convergence speed and comprehensive performance indicators compared to the unimproved DQN algorithm,with precision,recall,F_(1-score),and accuracy reaching up to 100%,98.07%,99.02%,and 98.50% respectively,and the model’s accuracy is higher than other machine learning algorithms like back propagation,support vector machine.展开更多
The increased interest in geothermal energy is evident,along with the exploitation of traditional hydrothermal systems,in the growing research and projects developing around the reuse of already-drilled oil,gas,and ex...The increased interest in geothermal energy is evident,along with the exploitation of traditional hydrothermal systems,in the growing research and projects developing around the reuse of already-drilled oil,gas,and exploration wells.The Republic of Croatia has around 4000 wells,however,due to a long period since most of these wells were drilled and completed,there is uncertainty about how many are available for retrofitting as deep-borehole heat exchangers.Nevertheless,as hydrocarbon production decreases,it is expected that the number of wells available for the revitalization and exploitation of geothermal energy will increase.The revitalization of wells via deep-borehole heat exchangers involves installing a coaxial heat exchanger and circulating the working fluid in a closed system,during which heat is transferred from the surrounding rock medium to the circulating fluid.Since drilled wells are not of uniformdepth and are located in areas with different thermal rock properties and geothermal gradients,an analysis was conducted to determine available thermal energy as a function of well depth,geothermal gradient,and circulating fluid flow rate.Additionally,an economic analysis was performed to determine the benefits of retrofitting existing assets,such as drilled wells,compared to drilling new wells to obtain the same amount of thermal energy.展开更多
Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressin...Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.展开更多
The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and na...The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R^(2)(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.展开更多
This study aimed to integrate Monte Carlo(MC)simulation with deep learning(DL)-based denoising techniques to achieve fast and accurate prediction of high-quality electronic portal imaging device(EPID)transmission dose...This study aimed to integrate Monte Carlo(MC)simulation with deep learning(DL)-based denoising techniques to achieve fast and accurate prediction of high-quality electronic portal imaging device(EPID)transmission dose(TD)for patientspecific quality assurance(PSQA).A total of 100 lung cases were used to obtain the noisy EPID TD by the ARCHER MC code under four kinds of particle numbers(1×10^(6),1×10^(7),1×10^(8)and 1×10^(9)),and the original EPID TD was denoised by the SUNet neural network.The denoised EPID TD was assessed both qualitatively and quantitatively using the structural similarity(SSIM),peak signal-to-noise ratio(PSNR),and gamma passing rate(GPR)with respect to 1×10^(9)as a reference.The computation times for both the MC simulation and DL-based denoising were recorded.As the number of particles increased,both the quality of the noisy EPID TD and computation time increased significantly(1×10^(6):1.12 s,1×10^(7):1.72 s,1×10^(8):8.62 s,and 1×10^(9):73.89 s).In contrast,the DL-based denoising time remained at 0.13-0.16 s.The denoised EPID TD shows a smoother visual appearance and profile curves,but differences between 1×10^(6)and 1×10^(9)still remain.SSIM improves from 0.61 to 0.95 for 1×10^(6),0.70 to 0.96 for 1×10^(7),and 0.90 to 0.97 for 1×10^(8).PSNR increases by>20%for 1×10^(6)and 1×10^(7),and>10%for 1×10^(8).GPR improves from 48.47%to 89.10%for 1×10^(6),61.04%to 94.35%for 1×10^(7),and 91.88%to 99.55%for 1×10^(8).The method that combines MC simulation with DL-based denoising for EPID TD generation can accelerate TD prediction and maintain high accuracy,offering a promising solution for efficient PSQA.展开更多
Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,...Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.展开更多
As urbanization continues to accelerate,the challenges associated with managing transportation in metropolitan areas become increasingly complex.The surge in population density contributes to traffic congestion,impact...As urbanization continues to accelerate,the challenges associated with managing transportation in metropolitan areas become increasingly complex.The surge in population density contributes to traffic congestion,impacting travel experiences and posing safety risks.Smart urban transportation management emerges as a strategic solution,conceptualized here as a multidimensional big data problem.The success of this strategy hinges on the effective collection of information from diverse,extensive,and heterogeneous data sources,necessitating the implementation of full⁃stack Information and Communication Technology(ICT)solutions.The main idea of the work is to investigate the current technologies of Intelligent Transportation Systems(ITS)and enhance the safety of urban transportation systems.Machine learning models,trained on historical data,can predict traffic congestion,allowing for the implementation of preventive measures.Deep learning architectures,with their ability to handle complex data representations,further refine traffic predictions,contributing to more accurate and dynamic transportation management.The background of this research underscores the challenges posed by traffic congestion in metropolitan areas and emphasizes the need for advanced technological solutions.By integrating GPS and GIS technologies with machine learning algorithms,this work aims to pay attention to the development of intelligent transportation systems that not only address current challenges but also pave the way for future advancements in urban transportation management.展开更多
The implementation of multifunctional application scenarios for mobile terminal devices has increased the energy density requirements of batteries.Increasing the charging voltage can rapidly increase the specific capa...The implementation of multifunctional application scenarios for mobile terminal devices has increased the energy density requirements of batteries.Increasing the charging voltage can rapidly increase the specific capacity of layered transition metal oxides;however,it also exacerbates the release of lattice oxygen and the contraction of the unit cell.Ternary materials are designed in a secondary particle state to meet the requirements of power battery applications.Therefore,to create ternary materials that can operate under ultrahigh voltages,attention should be given to both surface modification and particle integrity maintenance.By utilizing elemental selenium(Se)with a low melting point,easy sublimation,and multiple variable valence states,deep grain boundary modification was implemented inside the particles.The performance of the cathode material was evaluated through pouch cells,and the improvement mechanism was explored through molecular dynamics simulation calculations.Under the protection of a three-dimensional Se-rich modified layer,LiNi_(1/3)Co_(1/3)Mn_(1/3)O_(2)achieved stable operation at ultrahigh voltages(4.6 V vs.Li/Li^(+));a sacrificial protection mechanism based on the chronic decomposition of the Se-rich layer was proposed to explain the efficacy of Se modification in stabilizing ternary materials.This deep grain boundary modification based on elemental Se provides a new solution for the ultrahigh-voltage operation of transition metal oxides and provides a scientific basis and technical support for solving the interface contact problem of all-solid-state batteries.展开更多
The stator of the maglev track plays a crucial role in the operation of the maglev system.Currently,the efficiency of maglev track inspection is limited by several factors,including the large span of elevated structur...The stator of the maglev track plays a crucial role in the operation of the maglev system.Currently,the efficiency of maglev track inspection is limited by several factors,including the large span of elevated structures,manual visual inspection,short inspection window times,and limited GPS positioning accuracy.To address these issues,this paper proposes a deep learning-based method for detecting and locating stator surface damage.This study establishes a maglev track stator surface image dataset,trains different object detection models,and compares their performance.Ultimately,YOLO and ByteTrack object tracking algorithms were chosen as the basic framework and enhanced to achieve automatic identification of high-speed maglev track stator surface damage images and track and count stator surface localization feature images.By matching the identified damaged images with their corresponding stator segment and beam segment sequence numbers,the location of the damage is pinpointed to the corresponding stator segment,enabling rapid and accurate identification and localization of complex damage to the maglev track stator surface.展开更多
Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding,management,and quality testing.The current method of measuring seedling phe...Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding,management,and quality testing.The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient,subjective and destroys samples.Therefore,the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning.The Azure Kinect was used to shoot canopy color images,depth images,and RGB-D images of the watermelon plug seedlings.The Mask-RCNN network was used to classify,segment,and count the canopy leaves of the watermelon plug seedlings.To reduce the error of leaf area measurement caused by mutual occlusion of leaves,the leaves were repaired by CycleGAN,and the depth images were restored by image processing.Then,the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud.The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray.Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height.The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages.The average relative error of measurement is 2.33%for the number of true leaves,4.59%for the number of cotyledons,8.37%for the leaf area,and 3.27%for the plant height.The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.展开更多
In hybrid beamforming design using the conventional gradient projection(GP)algorithm,it is common to use a fixed step size,which results in a slow convergence rate and unsatisfactory achievable rate performance.This p...In hybrid beamforming design using the conventional gradient projection(GP)algorithm,it is common to use a fixed step size,which results in a slow convergence rate and unsatisfactory achievable rate performance.This paper employs a deep unfolding algorithm within a small fixed number of iterations to tackle the hybrid beamforming optimization problem.The optimal step size is obtained by combining the conventional GP algorithm with the deep learning technique,and every step in deep learning is explainable.Simulation results show that the proposed deep unfolding algorithm demonstrates a lower computational time and superior achievable rate performance than the conventional GP algorithm.展开更多
Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering.This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies,aiming to develop a f...Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering.This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies,aiming to develop a fatigue life prediction method for critical bogie components using deep learning models and measured track load spectra.Extensive field tests on Chengdu Metro Line 1 were conducted to acquire acceleration and stress response data of the wheelset lifting lug,generating training samples for the neural network system.Component stress responses were calculated via time-domain track acceleration and validated against in-situ stress measurements.Results show that neural network-fitted dynamic stress values exhibit excellent consistency with measured data,with errors constrained within 5%.This study validates the proposed small-sample deep learning approach as an effective and accurate solution for fatigue life prediction of critical bogie components under operational load conditions.展开更多
To counteract small sample size,severe class imbalance and high feature redundancy in 90-day mRS prediction after stroke,this study proposes a four-stage pipeline-“ADASYN re-sampling→clinical+statistical feature scr...To counteract small sample size,severe class imbalance and high feature redundancy in 90-day mRS prediction after stroke,this study proposes a four-stage pipeline-“ADASYN re-sampling→clinical+statistical feature screening→dimensionality reduction→5-fold cross-validation”-and benchmark composite deep-learning architectures.ADASYN first balances the minority classes in the original feature space.Next,a tri-level filter(clinical domain knowledge,variance threshold,mutual information)removes clinically meaningless or redundant variables,after which PCA compresses the remaining features while preserving critical neurological signatures(e.g.,brain-herniation history).Four hybrid CNN-RNN models are trained and compared under strict 5-fold cross-validation;the optimal ensemble yields stable,clinically interpretable probabilities that can support individualized rehabilitation planning.展开更多
文摘With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world tasks.Given the scope of this area,various techniques are found in the literature.One such notable technique,Multiple Deep Q-Network(DQN)based RL systems use multiple DQN-based-entities,which learn together and communicate with each other.The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed.As more complex DQNs come to the fore,the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training,need for high resources,more training time,and difficulty in fine-tuning leading to performance issues.Taking a cue from the parallel processing found in the nature and its efficacy,we propose a lightweight ensemble based approach for solving the core RL tasks.It uses multiple binary action DQNs having shared state and reward.The benefits of the proposed approach are overall simplicity,faster convergence and better performance compared to conventional DQN based approaches.The approach can potentially be extended to any type of DQN by forming its ensemble.Conducting extensive experimentation,promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks,and Atari 2600 games as compared to recent techniques.The proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task,259.2 on the LunarLander-v2 task,and state-of-the-art results on four out of five Atari 2600 games.
基金This work was funded by BK21 FOUR(Fostering Outstanding Universities for Research)(No.5199990914048)this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2020R1I1A3066543)In addition,this work was supported by the Soonchunhyang University Research Fund.
文摘Federated learning(FL)activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging processes.However,in large-scale heterogeneous Internet of Things(IoT)cellular networks,massive multi-dimensional model update iterations and resource-constrained computation are challenging aspects to be tackled significantly.This paper introduces the system model of converging softwaredefined networking(SDN)and network functions virtualization(NFV)to enable device/resource abstractions and provide NFV-enabled edge FL(eFL)aggregation servers for advancing automation and controllability.Multi-agent deep Q-networks(MADQNs)target to enforce a self-learning softwarization,optimize resource allocation policies,and advocate computation offloading decisions.With gathered network conditions and resource states,the proposed agent aims to explore various actions for estimating expected longterm rewards in a particular state observation.In exploration phase,optimal actions for joint resource allocation and offloading decisions in different possible states are obtained by maximum Q-value selections.Action-based virtual network functions(VNF)forwarding graph(VNFFG)is orchestrated to map VNFs towards eFL aggregation server with sufficient communication and computation resources in NFV infrastructure(NFVI).The proposed scheme indicates deficient allocation actions,modifies the VNF backup instances,and reallocates the virtual resource for exploitation phase.Deep neural network(DNN)is used as a value function approximator,and epsilongreedy algorithm balances exploration and exploitation.The scheme primarily considers the criticalities of FL model services and congestion states to optimize long-term policy.Simulation results presented the outperformance of the proposed scheme over reference schemes in terms of Quality of Service(QoS)performance metrics,including packet drop ratio,packet drop counts,packet delivery ratio,delay,and throughput.
基金Project ZR2023MF111 supported by Shandong Provincial Natural Science Foundation。
文摘With the advancement of Vehicle-to-Everything(V2X)technology,efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance.Existing methods suffer from high computational complexity and decision latency under high-density traffic and heterogeneous network conditions.To address these challenges,this study presents an innovative framework that combines Graph Neural Networks(GNNs)with a Double Deep Q-Network(DDQN),utilizing dynamic graph structures and reinforcement learning.An adaptive neighbor sampling mechanism is introduced to dynamically select the most relevant neighbors based on interference levels and network topology,thereby improving decision accuracy and efficiency.Meanwhile,the framework models communication links as nodes and interference relationships as edges,effectively capturing the direct impact of interference on resource allocation while reducing computational complexity and preserving critical interaction information.Employing an aggregation mechanism based on the Graph Attention Network(GAT),it dynamically adjusts the neighbor sampling scope and performs attention-weighted aggregation based on node importance,ensuring more efficient and adaptive resource management.This design ensures reliable Vehicle-to-Vehicle(V2V)communication while maintaining high Vehicle-to-Infrastructure(V2I)throughput.The framework retains the global feature learning capabilities of GNNs and supports distributed network deployment,allowing vehicles to extract low-dimensional graph embeddings from local observations for real-time resource decisions.Experimental results demonstrate that the proposed method significantly reduces computational overhead,mitigates latency,and improves resource utilization efficiency in vehicular networks under complex traffic scenarios.This research not only provides a novel solution to resource allocation challenges in V2X networks but also advances the application of DDQN in intelligent transportation systems,offering substantial theoretical significance and practical value.
文摘At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.
基金Supported by the National Natural Science Foundation of China under Grant Nos.52071099,52071104National Key Project of Research and Development Program under Grant No.2021YFC2801300Research Fund from National Key Laboratory of Autonomous Marine Vehicle Technology under Grant No.2023-SXJQR-SYSJJ01.
文摘The rudder mechanism of the X-rudder autonomous underwater cehicle(AUV)is relatively complex,and fault diagnosis capability is an important guarantee for its task execution in complex underwater environments.However,traditional fault diagnosis methods currently rely on prior knowledge and expert experience,and lack accuracy.In order to improve the autonomy and accuracy of fault diagnosis methods,and overcome the shortcomings of traditional algorithms,this paper proposes an X-steering AUV fault diagnosis model based on the deep reinforcement learning deep Q network(DQN)algorithm,which can learn the relationship between state data and fault types,map raw residual data to corresponding fault patterns,and achieve end-to-end mapping.In addition,to solve the problem of few X-steering fault sample data,Dropout technology is introduced during the model training phase to improve the performance of the DQN algorithm.Experimental results show that the proposed model has improved the convergence speed and comprehensive performance indicators compared to the unimproved DQN algorithm,with precision,recall,F_(1-score),and accuracy reaching up to 100%,98.07%,99.02%,and 98.50% respectively,and the model’s accuracy is higher than other machine learning algorithms like back propagation,support vector machine.
文摘The increased interest in geothermal energy is evident,along with the exploitation of traditional hydrothermal systems,in the growing research and projects developing around the reuse of already-drilled oil,gas,and exploration wells.The Republic of Croatia has around 4000 wells,however,due to a long period since most of these wells were drilled and completed,there is uncertainty about how many are available for retrofitting as deep-borehole heat exchangers.Nevertheless,as hydrocarbon production decreases,it is expected that the number of wells available for the revitalization and exploitation of geothermal energy will increase.The revitalization of wells via deep-borehole heat exchangers involves installing a coaxial heat exchanger and circulating the working fluid in a closed system,during which heat is transferred from the surrounding rock medium to the circulating fluid.Since drilled wells are not of uniformdepth and are located in areas with different thermal rock properties and geothermal gradients,an analysis was conducted to determine available thermal energy as a function of well depth,geothermal gradient,and circulating fluid flow rate.Additionally,an economic analysis was performed to determine the benefits of retrofitting existing assets,such as drilled wells,compared to drilling new wells to obtain the same amount of thermal energy.
基金the Collaborative Innovation Project of Shanghai,China for the financial support。
文摘Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.
基金supported by the Academic Research Projects of Beijing Union University(ZK20202204)the National Natural Science Foundation of China(12250005,12073040,12273059,11973056,12003051,11573037,12073041,11427901,11572005,11611530679 and 12473052)+1 种基金the Strategic Priority Research Program of the China Academy of Sciences(XDB0560000,XDA15052200,XDB09040200,XDA15010700,XDB0560301,and XDA15320102)the Chinese Meridian Project(CMP).
文摘The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R^(2)(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.
基金supported by National Key R&D Program of China(No.2022YFC2404604)Chongqing Research Institution Performance Incentive Guidance Special Project(No.CSTB2023JXJL-YFX0080)Chongqing Medical Scientific Research Project(Joint project of Chongqing Health Commission and Science and Technology Bureau)(No.2022DBXM005)。
文摘This study aimed to integrate Monte Carlo(MC)simulation with deep learning(DL)-based denoising techniques to achieve fast and accurate prediction of high-quality electronic portal imaging device(EPID)transmission dose(TD)for patientspecific quality assurance(PSQA).A total of 100 lung cases were used to obtain the noisy EPID TD by the ARCHER MC code under four kinds of particle numbers(1×10^(6),1×10^(7),1×10^(8)and 1×10^(9)),and the original EPID TD was denoised by the SUNet neural network.The denoised EPID TD was assessed both qualitatively and quantitatively using the structural similarity(SSIM),peak signal-to-noise ratio(PSNR),and gamma passing rate(GPR)with respect to 1×10^(9)as a reference.The computation times for both the MC simulation and DL-based denoising were recorded.As the number of particles increased,both the quality of the noisy EPID TD and computation time increased significantly(1×10^(6):1.12 s,1×10^(7):1.72 s,1×10^(8):8.62 s,and 1×10^(9):73.89 s).In contrast,the DL-based denoising time remained at 0.13-0.16 s.The denoised EPID TD shows a smoother visual appearance and profile curves,but differences between 1×10^(6)and 1×10^(9)still remain.SSIM improves from 0.61 to 0.95 for 1×10^(6),0.70 to 0.96 for 1×10^(7),and 0.90 to 0.97 for 1×10^(8).PSNR increases by>20%for 1×10^(6)and 1×10^(7),and>10%for 1×10^(8).GPR improves from 48.47%to 89.10%for 1×10^(6),61.04%to 94.35%for 1×10^(7),and 91.88%to 99.55%for 1×10^(8).The method that combines MC simulation with DL-based denoising for EPID TD generation can accelerate TD prediction and maintain high accuracy,offering a promising solution for efficient PSQA.
基金supported by the Basic Science Research Program(2023R1A2C3004336,RS-202300243807)&Regional Leading Research Center(RS-202400405278)through the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)。
文摘Wearable sensors integrated with deep learning techniques have the potential to revolutionize seamless human-machine interfaces for real-time health monitoring,clinical diagnosis,and robotic applications.Nevertheless,it remains a critical challenge to simultaneously achieve desirable mechanical and electrical performance along with biocompatibility,adhesion,self-healing,and environmental robustness with excellent sensing metrics.Herein,we report a multifunctional,anti-freezing,selfadhesive,and self-healable organogel pressure sensor composed of cobalt nanoparticle encapsulated nitrogen-doped carbon nanotubes(CoN CNT)embedded in a polyvinyl alcohol-gelatin(PVA/GLE)matrix.Fabricated using a binary solvent system of water and ethylene glycol(EG),the CoN CNT/PVA/GLE organogel exhibits excellent flexibility,biocompatibility,and temperature tolerance with remarkable environmental stability.Electrochemical impedance spectroscopy confirms near-stable performance across a broad humidity range(40%-95%RH).Freeze-tolerant conductivity under sub-zero conditions(-20℃)is attributed to the synergistic role of CoN CNT and EG,preserving mobility and network integrity.The Co N CNT/PVA/GLE organogel sensor exhibits high sensitivity of 5.75 k Pa^(-1)in the detection range from 0 to 20 k Pa,ideal for subtle biomechanical motion detection.A smart human-machine interface for English letter recognition using deep learning achieved 98%accuracy.The organogel sensor utility was extended to detect human gestures like finger bending,wrist motion,and throat vibration during speech.
文摘As urbanization continues to accelerate,the challenges associated with managing transportation in metropolitan areas become increasingly complex.The surge in population density contributes to traffic congestion,impacting travel experiences and posing safety risks.Smart urban transportation management emerges as a strategic solution,conceptualized here as a multidimensional big data problem.The success of this strategy hinges on the effective collection of information from diverse,extensive,and heterogeneous data sources,necessitating the implementation of full⁃stack Information and Communication Technology(ICT)solutions.The main idea of the work is to investigate the current technologies of Intelligent Transportation Systems(ITS)and enhance the safety of urban transportation systems.Machine learning models,trained on historical data,can predict traffic congestion,allowing for the implementation of preventive measures.Deep learning architectures,with their ability to handle complex data representations,further refine traffic predictions,contributing to more accurate and dynamic transportation management.The background of this research underscores the challenges posed by traffic congestion in metropolitan areas and emphasizes the need for advanced technological solutions.By integrating GPS and GIS technologies with machine learning algorithms,this work aims to pay attention to the development of intelligent transportation systems that not only address current challenges but also pave the way for future advancements in urban transportation management.
基金supported by the National Natural Science Foundation of China (52302259)the China Postdoctoral Science Foundation (CPSF) under Grant Number 2023M741479+4 种基金the Postdoctoral Fellowship Program of CPSF under Grant Number GZB20240280the Jiangxi Provincial Natural Science Foundation (20224ACB218006)the financial support from High-level Talent Research Special Funds of Jiangxi University of Science and Technology (Grant No. 205200100670)the Jiangxi Provincial Key Laboratory of Power Energy Storage Batteries and Materials (2024SSY10011)the Major Scientific and Technological Research R&D Special Project of Jiangxi Province(20244AFI92002)
文摘The implementation of multifunctional application scenarios for mobile terminal devices has increased the energy density requirements of batteries.Increasing the charging voltage can rapidly increase the specific capacity of layered transition metal oxides;however,it also exacerbates the release of lattice oxygen and the contraction of the unit cell.Ternary materials are designed in a secondary particle state to meet the requirements of power battery applications.Therefore,to create ternary materials that can operate under ultrahigh voltages,attention should be given to both surface modification and particle integrity maintenance.By utilizing elemental selenium(Se)with a low melting point,easy sublimation,and multiple variable valence states,deep grain boundary modification was implemented inside the particles.The performance of the cathode material was evaluated through pouch cells,and the improvement mechanism was explored through molecular dynamics simulation calculations.Under the protection of a three-dimensional Se-rich modified layer,LiNi_(1/3)Co_(1/3)Mn_(1/3)O_(2)achieved stable operation at ultrahigh voltages(4.6 V vs.Li/Li^(+));a sacrificial protection mechanism based on the chronic decomposition of the Se-rich layer was proposed to explain the efficacy of Se modification in stabilizing ternary materials.This deep grain boundary modification based on elemental Se provides a new solution for the ultrahigh-voltage operation of transition metal oxides and provides a scientific basis and technical support for solving the interface contact problem of all-solid-state batteries.
基金supported in part by the National Natural Science Foundation of China under Grant 52432012in part by the Shanghai Science and Technology Project with 25ZR1402508。
文摘The stator of the maglev track plays a crucial role in the operation of the maglev system.Currently,the efficiency of maglev track inspection is limited by several factors,including the large span of elevated structures,manual visual inspection,short inspection window times,and limited GPS positioning accuracy.To address these issues,this paper proposes a deep learning-based method for detecting and locating stator surface damage.This study establishes a maglev track stator surface image dataset,trains different object detection models,and compares their performance.Ultimately,YOLO and ByteTrack object tracking algorithms were chosen as the basic framework and enhanced to achieve automatic identification of high-speed maglev track stator surface damage images and track and count stator surface localization feature images.By matching the identified damaged images with their corresponding stator segment and beam segment sequence numbers,the location of the damage is pinpointed to the corresponding stator segment,enabling rapid and accurate identification and localization of complex damage to the maglev track stator surface.
基金funded by the National Key Research and Development Program of China(Grant No.2019YFD1001900)the HZAU-AGIS Cooperation Fund(Grant No.SZYJY2022006).
文摘Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding,management,and quality testing.The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient,subjective and destroys samples.Therefore,the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning.The Azure Kinect was used to shoot canopy color images,depth images,and RGB-D images of the watermelon plug seedlings.The Mask-RCNN network was used to classify,segment,and count the canopy leaves of the watermelon plug seedlings.To reduce the error of leaf area measurement caused by mutual occlusion of leaves,the leaves were repaired by CycleGAN,and the depth images were restored by image processing.Then,the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud.The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray.Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height.The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages.The average relative error of measurement is 2.33%for the number of true leaves,4.59%for the number of cotyledons,8.37%for the leaf area,and 3.27%for the plant height.The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.
基金STU Scientific Research Foundation for Talents under Grants NTF21048。
文摘In hybrid beamforming design using the conventional gradient projection(GP)algorithm,it is common to use a fixed step size,which results in a slow convergence rate and unsatisfactory achievable rate performance.This paper employs a deep unfolding algorithm within a small fixed number of iterations to tackle the hybrid beamforming optimization problem.The optimal step size is obtained by combining the conventional GP algorithm with the deep learning technique,and every step in deep learning is explainable.Simulation results show that the proposed deep unfolding algorithm demonstrates a lower computational time and superior achievable rate performance than the conventional GP algorithm.
基金supported by the CRRC Original Technology TenYear Cultivation Program(Grant No.2022CYY007)。
文摘Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering.This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies,aiming to develop a fatigue life prediction method for critical bogie components using deep learning models and measured track load spectra.Extensive field tests on Chengdu Metro Line 1 were conducted to acquire acceleration and stress response data of the wheelset lifting lug,generating training samples for the neural network system.Component stress responses were calculated via time-domain track acceleration and validated against in-situ stress measurements.Results show that neural network-fitted dynamic stress values exhibit excellent consistency with measured data,with errors constrained within 5%.This study validates the proposed small-sample deep learning approach as an effective and accurate solution for fatigue life prediction of critical bogie components under operational load conditions.
基金Shanghai University of Engineering Science Undergraduate Innovation Training Program(Project No.:cx2521005)。
文摘To counteract small sample size,severe class imbalance and high feature redundancy in 90-day mRS prediction after stroke,this study proposes a four-stage pipeline-“ADASYN re-sampling→clinical+statistical feature screening→dimensionality reduction→5-fold cross-validation”-and benchmark composite deep-learning architectures.ADASYN first balances the minority classes in the original feature space.Next,a tri-level filter(clinical domain knowledge,variance threshold,mutual information)removes clinically meaningless or redundant variables,after which PCA compresses the remaining features while preserving critical neurological signatures(e.g.,brain-herniation history).Four hybrid CNN-RNN models are trained and compared under strict 5-fold cross-validation;the optimal ensemble yields stable,clinically interpretable probabilities that can support individualized rehabilitation planning.
