Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challeng...Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challenge due to factors such as light scattering,absorption,restricted visibility,and ambient noise.The advancement of deep learning has introduced powerful techniques for processing large amounts of unstructured and imperfect data collected from underwater environments.This study evaluated the efficacy of the You Only Look Once(YOLO)algorithm,a real-time object detection and localization model based on convolutional neural networks,in identifying and classifying various types of pipeline defects in underwater settings.YOLOv8,the latest evolution in the YOLO family,integrates advanced capabilities,such as anchor-free detection,a cross-stage partial network backbone for efficient feature extraction,and a feature pyramid network+path aggregation network neck for robust multi-scale object detection,which make it particularly well-suited for complex underwater environments.Due to the lack of suitable open-access datasets for underwater pipeline defects,a custom dataset was captured using a remotely operated vehicle in a controlled environment.This application has the following assets available for use.Extensive experimentation demonstrated that YOLOv8 X-Large consistently outperformed other models in terms of pipe defect detection and classification and achieved a strong balance between precision and recall in identifying pipeline cracks,rust,corners,defective welds,flanges,tapes,and holes.This research establishes the baseline performance of YOLOv8 for underwater defect detection and showcases its potential to enhance the reliability and efficiency of pipeline inspection tasks in challenging underwater environments.展开更多
AlGaN-based deep-ultraviolet(DUV)laser diodes(LDs)face performance challenges due to elec-tron leakage and poor hole injection which is often worsened by polarization effects from conventional elec-tron blocking layer...AlGaN-based deep-ultraviolet(DUV)laser diodes(LDs)face performance challenges due to elec-tron leakage and poor hole injection which is often worsened by polarization effects from conventional elec-tron blocking layers(EBLs).To overcome these limitations,we propose an EBL-free DUV LD design incor-porating a 1-nm undoped Al_(0.8)Ga_(0.2)N thin strip layer after the last quantum barrier.Using PICS3D simula-tions,we evaluate the optical and electrical characteristics.Results show a significant increase in effective electron barrier height(from 158.2 meV to 420.7 meV)and a reduction in hole barrier height(from 149.2 meV to 62.8 meV),which enhance hole injection and reduce electron leakage.The optimized structure(LD3)achieves a 14%increase in output power,improved slope efficiency(1.85 W/A),and lower threshold current.This design also reduces the quantum confined Stark effect and forms dual hole accumulation regions,im-proving recombination efficiency.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
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
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
To investigate the long-term fracture conductivity behavior of propped fractures under the high-temperature and high-pressure conditions of deep shale gas reservoirs in the Sichuan Basin,this study systematically anal...To investigate the long-term fracture conductivity behavior of propped fractures under the high-temperature and high-pressure conditions of deep shale gas reservoirs in the Sichuan Basin,this study systematically analyzed the effects of closure stress,proppant concentration,formation temperature,and proppant size combination.Conductivity experiments were conducted using the HXDL-2C long-term proppant conductivity evaluation system under simulated reservoir conditions to determine the time-dependent evolution of fracture conductivity.The results showed that the 50-h conductivity retention of the rock-plate experiments ranged from 22%to 28%.With increasing closure stress,fracture conductivity exhibited a rapid decline.Under a formation temperature of 120℃ and a proppant concentration of 5 kg·m^(-2),the short-term conductivity of 70/140 mesh quartz-sand-propped fractures was 2.37μm^(2)·cm,which decreased to 0.66μm^(2)·cm after long-term testing.When the closure stress increased to 80 MPa,the short-term and long-term conductivities further declined to 1.36μm^(2)·cm and 0.39μm^(2)·cm,respectively.Increasing the proppant concentration from 5 to 7.5 kg·m^(-2)at 120℃ and 80 MPa improved both short-term and long-term conductivities by enlarging the effective fracture width;however,the conductivity decay rate accelerated,and the 50-h retention dropped from 27.2%to 22.8%.Raising the temperature from 120℃ to 140℃ promoted proppant crushing and compaction,intensified shale creep,and accelerated fracture closure,reducing long-term conductivity from 0.37 to 0.30μm^(2)·cm.Under identical conditions,40/70 mesh ceramic proppants maintained significantly higher conductivities than 70/140 mesh quartz sand,with short-term and long-term values of 8.71 and 2.19μm^(2)·cm,respectively,at 120℃,80 MPa,and 5 kg·m^(-2).Pure quartz-sand systems failed to maintain effective conductivity under high-temperature and high-stress conditions,whereas adding 20%40/70 mesh ceramic proppant and thoroughly mixing it,the long-term conductivity has increased by 2.3 times,improving fracture stability while reducing overall cost.A predictive equation was derived from the experimental results to capture the dynamic decay characteristics of fracture conductivity.These outcomes provide a valuable experimental basis and technical support for optimizing fracturing fluid design,proppant selection,and operation parameters in deep shale formations.展开更多
This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limita...This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limitation of deep learning models—this study introduces a hybrid perception architecture that incorporates expertdriven LiDAR processing techniques into the deep neural network.Traditional 3DLiDAR processingmethods typically remove ground planes and apply distance-or density-based clustering for object detection.In this work,such expert knowledge is encoded as feature-level inputs and fused with the deep network,therebymitigating the data dependency issue of conventional learning-based approaches.Specifically,the proposedmethod combines two expert algorithms—Patchwork++for ground segmentation and DBSCAN for clustering—with a PointPillars-based LiDAR detection network.We design four hybrid versions of the network depending on the stage and method of integrating expert features into the feature map of the deep model.Among these,Version 4 incorporates a modified neck structure in PointPillars and introduces a new Cluster 2D Pseudo-Map Branch that utilizes cluster-level pseudo-images generated from Patchwork++and DBSCAN.This version achieved a+3.88%improvement mean Average Precision(mAP)compared to the baseline PointPillars.The results demonstrate that embedding expert-based perception logic into deep neural architectures can effectively enhance performance and reduce dependency on extensive training datasets,offering a promising direction for robust 3D LiDAR object detection in real-world scenarios.展开更多
To achieve the potential performance gain of massive multiple-input multiple-output(MIMO)systems,base stations(BS)require downlink channel state information(CSI)fed back by users to execute beamforming design,especial...To achieve the potential performance gain of massive multiple-input multiple-output(MIMO)systems,base stations(BS)require downlink channel state information(CSI)fed back by users to execute beamforming design,especially in the frequency division duplex(FDD)systems.However,due to the enormous number of antennas in massive MIMO systems,the feedback overhead of downlink CSI acquisition is extremely large.To address this issue,deep learning(DL)techniques have been introduced to de velop high-accuracy feedback strategies under limited backhaul constraints.In this paper,we provide an overview of DL-based CSI compression and feedback approaches in massive MIMO systems.Specifically,we introduce the conventional CSI compression and feedback schemes and the existing problems.Besides,we elaborate on various DL techniques employed in CSI compression from the perspective of network architecture and analyze the advantages of different techniques.We also enumerate the applications of DL-based methods for solving practical challenges in CSI compression and feedback.In addition,we brief the remaining issues in deep CSI compression and indicate potential directions in future wireless networks.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
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
Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(O...Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(OGTT),and fasting plasma glucose(FPG)screening techniques,which are invasive and limited in scale.Machine learning(ML)and deep neural network(DNN)models that use large datasets to learn the complex,nonlinear feature interactions,but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy.Conversely,DNN models are more robust,though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge.For predicting diabetes,this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory(BiLSTM)network with a bidirectional gated recurrent unit(BiGRU).A robust DL model,developed by combining various datasets with weighted coefficients,dense operations in the connection of deep layers,and the output aggregation using batch normalization and dropout functions to avoid overfitting.The goal of this hybrid model is better generalization and consistency among various datasets,which facilitates the effective management and early intervention.The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks.The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics.展开更多
Intelligent inspection of transmission lines enables efficient automated fault detection by integrating artificial intelligence,robotics,and other related technologies.It plays a key role in ensuring power grid safety...Intelligent inspection of transmission lines enables efficient automated fault detection by integrating artificial intelligence,robotics,and other related technologies.It plays a key role in ensuring power grid safety,reducing operation and maintenance costs,driving the digital transformation of the power industry,and facilitating the achievement of the dual-carbon goals.This review focuses on vision-based power line inspection,with deep learning as the core perspective to systematically analyze the latest research advancements in this field.Firstly,at the technical foundation level,it elaborates on deep learning algorithms for intelligent transmission line inspection based on image perception,covering object detection algorithms,semantic segmentation algorithms,and other relevant methodologies.Secondly,in application practice,it summarizes deep learning-based intelligent inspection applications across six dimensions—including detection of power insulators and their defects,transmission tower detection,power line feature extraction,metal fitting and defect detection,thermal fault diagnosis of power components,and safety hazard detection in power scenarios,and further lists relevant public datasets.Finally,in response to current challenges,it identifies five key future research directions,such as the deep integration of multiple learning paradigms,multi-modal data fusion,collaborative application of large and small models,cloud-edge-end collaborative integration,and multi-agent cluster control.This paper reviews and analyzes numerous deep learning-based intelligent detectionmethods for aerial images,comprehensively explores the application of deep learning in Unmanned Aerial Vehicle(UAV)inspection scenarios,and thus provides valuable theoretical and practical references for scholars engaged in smart grid automated inspection research.展开更多
Deep learning attentionmechanisms have achieved remarkable progress in computer vision,but still face limitations when handling images with ambiguous boundaries and uncertain feature representations.Conventional atten...Deep learning attentionmechanisms have achieved remarkable progress in computer vision,but still face limitations when handling images with ambiguous boundaries and uncertain feature representations.Conventional attention modules such as SE-Net,CBAM,ECA-Net,and CA adopt a deterministic paradigm,assigning fixed scalar weights to features without modeling ambiguity or confidence.To overcome these limitations,this paper proposes the Fuzzy Attention Network Layer(FANL),which integrates intuitionistic fuzzy set theory with convolutional neural networks to explicitly represent feature uncertainty through membership(μ),non-membership(ν),and hesitation(π)degrees.FANLconsists of four coremodules:(1)feature dimensionality reduction via global pooling,(2)fuzzymodeling using learnable clustering centers,(3)adaptive attention generation through weighted fusion of fuzzy components,and(4)feature refinement through residual connections.A cross-layer guidance mechanism is further introduced to enhance hierarchical feature propagation,allowing high-level semantic features to incorporate fine-grained texture information from shallow layers.Comprehensive experiments on three benchmark datasets—PathMNIST-30000,full PathMNIST,and Blood MNIST—demonstrate the effectiveness and generalizability of FANL.The model achieves 84.41±0.56%accuracy and a 1.69%improvement over the baseline CNN while maintaining lightweight computational complexity.Ablation studies show that removing any component causes a 1.7%–2.0%performance drop,validating the synergistic contribution of each module.Furthermore,FANL provides superior uncertainty calibration(ECE=0.0452)and interpretable selective prediction under uncertainty.Overall,FANL presents an efficient and uncertaintyaware attention framework that improves both accuracy and reliability,offering a promising direction for robust visual recognition under ambiguous or noisy conditions.展开更多
Accurate streamflow prediction is essential for flood warning,reservoir operation,irrigation scheduling,hydropower planning,and sustainable water management,yet remains challenging due to the complexity of hydrologica...Accurate streamflow prediction is essential for flood warning,reservoir operation,irrigation scheduling,hydropower planning,and sustainable water management,yet remains challenging due to the complexity of hydrological processes.Although data-driven models often outperform conventional physics-based hydrological modelling approaches,their real-world deployment is limited by cost,infrastructure demands,and the interdisciplinary expertise required.To bridge this gap,this study developed QPred,a regional,lightweight,cost-effective,web-delivered application for daily streamflow forecasting.The study executed an end-to-end workflow,from field data acquisition to accessible web-based deployment for on-demand forecasting.High-resolution rainfall data were recorded with tippingbucket gauges and loggers,while river water depth in the Aglar and Paligaad watersheds was converted to discharge using site-specific rating curves,resulting in a daily dataset of precipitation,river water level and discharge.Four DL architectures were trained,including vanilla Long Short-Term Memory(LSTM),stacked LSTM,bidirectional LSTM,and Gated Recurrent Unit(GRU),and evaluated using Nash-Sutcliffe Efficiency(NSE),Coefficient of Determination(R2),Root-Mean-Square-Error-Standard-Deviation Ratio(RSR),and Percentage Bias(PBIAS)metrics.Performance was watershed-specific,as the vanilla LSTM demonstrated the best generalisation for the Aglar watershed(R2=0.88,NSE=0.82,RMSE=0.12 during validation),while the GRU achieved the highest validation accuracy in Paligaad(R2=0.88,NSE=0.88,RMSE=0.49).All models achieved satisfactory to excellent performance during calibration(R2>0.91,NSE>0.91 for both watersheds),demonstrating strong capability to capture streamflow dynamics.The highest performing models were selected and embedded into the QPred application.QPred was developed as a lightweight web pipeline,utilising Google Colab as the primary execution environment,Flask as the backend inference framework,Google Drive for artefact storage,andNgrok for secureHTTPS tunnelling.Auser-friendly front end utilises range sliders(bounded by observed minima and maxima)to gather inputs and provides discharge data along with metadata,thereby enhancing transparency.This work demonstrates that accurate,context-aware deep learningmodels can be delivered through low-cost,web-based platforms,providing a reproducible and scalable pipeline for hydrological applications in other watersheds and for practitioners.展开更多
文摘Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challenge due to factors such as light scattering,absorption,restricted visibility,and ambient noise.The advancement of deep learning has introduced powerful techniques for processing large amounts of unstructured and imperfect data collected from underwater environments.This study evaluated the efficacy of the You Only Look Once(YOLO)algorithm,a real-time object detection and localization model based on convolutional neural networks,in identifying and classifying various types of pipeline defects in underwater settings.YOLOv8,the latest evolution in the YOLO family,integrates advanced capabilities,such as anchor-free detection,a cross-stage partial network backbone for efficient feature extraction,and a feature pyramid network+path aggregation network neck for robust multi-scale object detection,which make it particularly well-suited for complex underwater environments.Due to the lack of suitable open-access datasets for underwater pipeline defects,a custom dataset was captured using a remotely operated vehicle in a controlled environment.This application has the following assets available for use.Extensive experimentation demonstrated that YOLOv8 X-Large consistently outperformed other models in terms of pipe defect detection and classification and achieved a strong balance between precision and recall in identifying pipeline cracks,rust,corners,defective welds,flanges,tapes,and holes.This research establishes the baseline performance of YOLOv8 for underwater defect detection and showcases its potential to enhance the reliability and efficiency of pipeline inspection tasks in challenging underwater environments.
文摘AlGaN-based deep-ultraviolet(DUV)laser diodes(LDs)face performance challenges due to elec-tron leakage and poor hole injection which is often worsened by polarization effects from conventional elec-tron blocking layers(EBLs).To overcome these limitations,we propose an EBL-free DUV LD design incor-porating a 1-nm undoped Al_(0.8)Ga_(0.2)N thin strip layer after the last quantum barrier.Using PICS3D simula-tions,we evaluate the optical and electrical characteristics.Results show a significant increase in effective electron barrier height(from 158.2 meV to 420.7 meV)and a reduction in hole barrier height(from 149.2 meV to 62.8 meV),which enhance hole injection and reduce electron leakage.The optimized structure(LD3)achieves a 14%increase in output power,improved slope efficiency(1.85 W/A),and lower threshold current.This design also reduces the quantum confined Stark effect and forms dual hole accumulation regions,im-proving recombination efficiency.
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.
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funding for this research comes fromHubei Provincial Natural Science Foundation(2022CFB690)the Open Foundation(UOG2024-03)of Cooperative Innovation Center of Unconventional Oil+1 种基金Gas,Yangtze University(Ministry of Education&Hubei Province)and the Open Foundation(YQZC202302)of Hubei Key Laboratory of Oil and Gas Drilling and Production Engineering(Yangtze University)the National Natural Science Foundation of China(Grant no.U23B20156).
文摘To investigate the long-term fracture conductivity behavior of propped fractures under the high-temperature and high-pressure conditions of deep shale gas reservoirs in the Sichuan Basin,this study systematically analyzed the effects of closure stress,proppant concentration,formation temperature,and proppant size combination.Conductivity experiments were conducted using the HXDL-2C long-term proppant conductivity evaluation system under simulated reservoir conditions to determine the time-dependent evolution of fracture conductivity.The results showed that the 50-h conductivity retention of the rock-plate experiments ranged from 22%to 28%.With increasing closure stress,fracture conductivity exhibited a rapid decline.Under a formation temperature of 120℃ and a proppant concentration of 5 kg·m^(-2),the short-term conductivity of 70/140 mesh quartz-sand-propped fractures was 2.37μm^(2)·cm,which decreased to 0.66μm^(2)·cm after long-term testing.When the closure stress increased to 80 MPa,the short-term and long-term conductivities further declined to 1.36μm^(2)·cm and 0.39μm^(2)·cm,respectively.Increasing the proppant concentration from 5 to 7.5 kg·m^(-2)at 120℃ and 80 MPa improved both short-term and long-term conductivities by enlarging the effective fracture width;however,the conductivity decay rate accelerated,and the 50-h retention dropped from 27.2%to 22.8%.Raising the temperature from 120℃ to 140℃ promoted proppant crushing and compaction,intensified shale creep,and accelerated fracture closure,reducing long-term conductivity from 0.37 to 0.30μm^(2)·cm.Under identical conditions,40/70 mesh ceramic proppants maintained significantly higher conductivities than 70/140 mesh quartz sand,with short-term and long-term values of 8.71 and 2.19μm^(2)·cm,respectively,at 120℃,80 MPa,and 5 kg·m^(-2).Pure quartz-sand systems failed to maintain effective conductivity under high-temperature and high-stress conditions,whereas adding 20%40/70 mesh ceramic proppant and thoroughly mixing it,the long-term conductivity has increased by 2.3 times,improving fracture stability while reducing overall cost.A predictive equation was derived from the experimental results to capture the dynamic decay characteristics of fracture conductivity.These outcomes provide a valuable experimental basis and technical support for optimizing fracturing fluid design,proppant selection,and operation parameters in deep shale formations.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00245084)by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(RS-2024-00415938,HRD Program for Industrial Innovation)and Soonchunhyang University.
文摘This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles.To address the high dependency on large-scale annotated data—an inherent limitation of deep learning models—this study introduces a hybrid perception architecture that incorporates expertdriven LiDAR processing techniques into the deep neural network.Traditional 3DLiDAR processingmethods typically remove ground planes and apply distance-or density-based clustering for object detection.In this work,such expert knowledge is encoded as feature-level inputs and fused with the deep network,therebymitigating the data dependency issue of conventional learning-based approaches.Specifically,the proposedmethod combines two expert algorithms—Patchwork++for ground segmentation and DBSCAN for clustering—with a PointPillars-based LiDAR detection network.We design four hybrid versions of the network depending on the stage and method of integrating expert features into the feature map of the deep model.Among these,Version 4 incorporates a modified neck structure in PointPillars and introduces a new Cluster 2D Pseudo-Map Branch that utilizes cluster-level pseudo-images generated from Patchwork++and DBSCAN.This version achieved a+3.88%improvement mean Average Precision(mAP)compared to the baseline PointPillars.The results demonstrate that embedding expert-based perception logic into deep neural architectures can effectively enhance performance and reduce dependency on extensive training datasets,offering a promising direction for robust 3D LiDAR object detection in real-world scenarios.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20240319003the NSFC under Grant No.62571112。
文摘To achieve the potential performance gain of massive multiple-input multiple-output(MIMO)systems,base stations(BS)require downlink channel state information(CSI)fed back by users to execute beamforming design,especially in the frequency division duplex(FDD)systems.However,due to the enormous number of antennas in massive MIMO systems,the feedback overhead of downlink CSI acquisition is extremely large.To address this issue,deep learning(DL)techniques have been introduced to de velop high-accuracy feedback strategies under limited backhaul constraints.In this paper,we provide an overview of DL-based CSI compression and feedback approaches in massive MIMO systems.Specifically,we introduce the conventional CSI compression and feedback schemes and the existing problems.Besides,we elaborate on various DL techniques employed in CSI compression from the perspective of network architecture and analyze the advantages of different techniques.We also enumerate the applications of DL-based methods for solving practical challenges in CSI compression and feedback.In addition,we brief the remaining issues in deep CSI compression and indicate potential directions in future wireless networks.
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 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.
基金supported by the School of Digital Science,Universiti Brunei Darussalam,Brunei.
文摘Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(OGTT),and fasting plasma glucose(FPG)screening techniques,which are invasive and limited in scale.Machine learning(ML)and deep neural network(DNN)models that use large datasets to learn the complex,nonlinear feature interactions,but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy.Conversely,DNN models are more robust,though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge.For predicting diabetes,this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory(BiLSTM)network with a bidirectional gated recurrent unit(BiGRU).A robust DL model,developed by combining various datasets with weighted coefficients,dense operations in the connection of deep layers,and the output aggregation using batch normalization and dropout functions to avoid overfitting.The goal of this hybrid model is better generalization and consistency among various datasets,which facilitates the effective management and early intervention.The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks.The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics.
基金financially supported by theNatural Research Project of College in Anhui Province under grant 2024AH051365,2025AHGXZK30826Research Platform of New Energy and Energy-Saving Technology Research Center under grant KYJG002.
文摘Intelligent inspection of transmission lines enables efficient automated fault detection by integrating artificial intelligence,robotics,and other related technologies.It plays a key role in ensuring power grid safety,reducing operation and maintenance costs,driving the digital transformation of the power industry,and facilitating the achievement of the dual-carbon goals.This review focuses on vision-based power line inspection,with deep learning as the core perspective to systematically analyze the latest research advancements in this field.Firstly,at the technical foundation level,it elaborates on deep learning algorithms for intelligent transmission line inspection based on image perception,covering object detection algorithms,semantic segmentation algorithms,and other relevant methodologies.Secondly,in application practice,it summarizes deep learning-based intelligent inspection applications across six dimensions—including detection of power insulators and their defects,transmission tower detection,power line feature extraction,metal fitting and defect detection,thermal fault diagnosis of power components,and safety hazard detection in power scenarios,and further lists relevant public datasets.Finally,in response to current challenges,it identifies five key future research directions,such as the deep integration of multiple learning paradigms,multi-modal data fusion,collaborative application of large and small models,cloud-edge-end collaborative integration,and multi-agent cluster control.This paper reviews and analyzes numerous deep learning-based intelligent detectionmethods for aerial images,comprehensively explores the application of deep learning in Unmanned Aerial Vehicle(UAV)inspection scenarios,and thus provides valuable theoretical and practical references for scholars engaged in smart grid automated inspection research.
文摘Deep learning attentionmechanisms have achieved remarkable progress in computer vision,but still face limitations when handling images with ambiguous boundaries and uncertain feature representations.Conventional attention modules such as SE-Net,CBAM,ECA-Net,and CA adopt a deterministic paradigm,assigning fixed scalar weights to features without modeling ambiguity or confidence.To overcome these limitations,this paper proposes the Fuzzy Attention Network Layer(FANL),which integrates intuitionistic fuzzy set theory with convolutional neural networks to explicitly represent feature uncertainty through membership(μ),non-membership(ν),and hesitation(π)degrees.FANLconsists of four coremodules:(1)feature dimensionality reduction via global pooling,(2)fuzzymodeling using learnable clustering centers,(3)adaptive attention generation through weighted fusion of fuzzy components,and(4)feature refinement through residual connections.A cross-layer guidance mechanism is further introduced to enhance hierarchical feature propagation,allowing high-level semantic features to incorporate fine-grained texture information from shallow layers.Comprehensive experiments on three benchmark datasets—PathMNIST-30000,full PathMNIST,and Blood MNIST—demonstrate the effectiveness and generalizability of FANL.The model achieves 84.41±0.56%accuracy and a 1.69%improvement over the baseline CNN while maintaining lightweight computational complexity.Ablation studies show that removing any component causes a 1.7%–2.0%performance drop,validating the synergistic contribution of each module.Furthermore,FANL provides superior uncertainty calibration(ECE=0.0452)and interpretable selective prediction under uncertainty.Overall,FANL presents an efficient and uncertaintyaware attention framework that improves both accuracy and reliability,offering a promising direction for robust visual recognition under ambiguous or noisy conditions.
文摘Accurate streamflow prediction is essential for flood warning,reservoir operation,irrigation scheduling,hydropower planning,and sustainable water management,yet remains challenging due to the complexity of hydrological processes.Although data-driven models often outperform conventional physics-based hydrological modelling approaches,their real-world deployment is limited by cost,infrastructure demands,and the interdisciplinary expertise required.To bridge this gap,this study developed QPred,a regional,lightweight,cost-effective,web-delivered application for daily streamflow forecasting.The study executed an end-to-end workflow,from field data acquisition to accessible web-based deployment for on-demand forecasting.High-resolution rainfall data were recorded with tippingbucket gauges and loggers,while river water depth in the Aglar and Paligaad watersheds was converted to discharge using site-specific rating curves,resulting in a daily dataset of precipitation,river water level and discharge.Four DL architectures were trained,including vanilla Long Short-Term Memory(LSTM),stacked LSTM,bidirectional LSTM,and Gated Recurrent Unit(GRU),and evaluated using Nash-Sutcliffe Efficiency(NSE),Coefficient of Determination(R2),Root-Mean-Square-Error-Standard-Deviation Ratio(RSR),and Percentage Bias(PBIAS)metrics.Performance was watershed-specific,as the vanilla LSTM demonstrated the best generalisation for the Aglar watershed(R2=0.88,NSE=0.82,RMSE=0.12 during validation),while the GRU achieved the highest validation accuracy in Paligaad(R2=0.88,NSE=0.88,RMSE=0.49).All models achieved satisfactory to excellent performance during calibration(R2>0.91,NSE>0.91 for both watersheds),demonstrating strong capability to capture streamflow dynamics.The highest performing models were selected and embedded into the QPred application.QPred was developed as a lightweight web pipeline,utilising Google Colab as the primary execution environment,Flask as the backend inference framework,Google Drive for artefact storage,andNgrok for secureHTTPS tunnelling.Auser-friendly front end utilises range sliders(bounded by observed minima and maxima)to gather inputs and provides discharge data along with metadata,thereby enhancing transparency.This work demonstrates that accurate,context-aware deep learningmodels can be delivered through low-cost,web-based platforms,providing a reproducible and scalable pipeline for hydrological applications in other watersheds and for practitioners.
