The coexistence of emerging containments,such as antibiotic resistant bacteria(ARB),antibiotic-resistant genes(ARGs)and antibiotics,potentially influence elimination efficiencies in UV light-emitting diode(UV-LED)alon...The coexistence of emerging containments,such as antibiotic resistant bacteria(ARB),antibiotic-resistant genes(ARGs)and antibiotics,potentially influence elimination efficiencies in UV light-emitting diode(UV-LED)alone and UV-LED/H_(2)O_(2) system as their complex interactions.Tetracycline(TC)degradation efficiency(kF)correlated closely with its UV molar absorbance(R^(2)=0.831)in UV-LED alone system and with·OH yield(R^(2)=0.999)in UV-LED/H_(2)O_(2) system across studied wavelengths(265,280 and 310 nm).The kF values for intracellular DNA(i-ARGs)also exhibited a high correlation with UV-LED wavelengths in both systems(R^(2)=0.997-0.999).The coexistence of TC and ARB/ARGs resulted in a mutual inhibition of their degradation efficiencies due to competition for photons and·OH,along with the consequent reduction in intracellular ROS within ARB,with their degradation efficiencies exhibiting marked dependence on wavelength in both systems.Notably,the UV-LED/H_(2)O_(2) system at 265 nm effectively achieved the simultaneous removal of TC,ARB and ARGs with minimal energy consumption,and successfully fragmented ARGs.The degradation pathway of TC was analyzed,and the biotoxicity of its degradation intermediates demonstrated the environmental friendliness and safety of UV-LED/H_(2)O_(2) technology.This study elucidated the competitive interactions between antibiotics and ARB/ARGs within UV-LED/H_(2)O_(2) system,providing a promising approach for their simultaneous removal while ensuring energy efficiency.展开更多
High-quality AlN layers with low-density threading dislocations are indispensable for high-efficiency deep ultraviolet light-emitting diodes（UV-LEDs）. In this work, a high-temperature AlN epitaxial layer was grown o...High-quality AlN layers with low-density threading dislocations are indispensable for high-efficiency deep ultraviolet light-emitting diodes（UV-LEDs）. In this work, a high-temperature AlN epitaxial layer was grown on sputtered AlN layer（used as nucleation layer, SNL） by a high-yield industrial metalorganic vapor phase epitaxy（MOVPE）. The full width half maximum（FWHM） of the rocking curve shows that the AlN epitaxial layer with SNL has good crystal quality. Furthermore, the relationships between the thickness of SNL and the FWHM values of（002） and（102） peaks were also studied. Finally, utilizing an SNL to enhance the quality of the epitaxial layer, deep UV-LEDs at 282 nm were successfully realized on sapphire substrate by the high-yield industrial MOVPE. The light-output power（LOP） of a deep UV-LED reaches 1.65 mW at 20 mA with external quantum efficiency of 1.87%. In addition, the saturation LOP of the deep UV-LED is 4.31 mW at an injection current of 60 mA. Hence, our studies supply a possible process to grow commercial deep UV-LEDs in high throughput industrial MOVPE, which can increase yield, at lower cost.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
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
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
In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep le...In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.展开更多
Green solvent pretreatment of biomass represents a promising ap-proach for enhancing the econom-ic value of lignocellulosic deriva-tives.In this study,corncob biomass was treated with a diol-based deep eutectic solven...Green solvent pretreatment of biomass represents a promising ap-proach for enhancing the econom-ic value of lignocellulosic deriva-tives.In this study,corncob biomass was treated with a diol-based deep eutectic solvent(DES)under mild conditions,facilitating efficient cellulose separation.The extracted cellulose was subsequently used to fabricate cellulose hydrogels in an aqueous zinc chloride solution.The resulting hydrogel exhibited a“water-in-salt”effect due to the high concentration of ZnCl_(2).Leveraging the antifreeze properties of sorbitol,the system demon-strated outstanding low-temperature electrochemical performance,including a broad operat-ing voltage window and an ionic conductivity of 38.4 mS·cm^(-1)at-20℃.At 20℃,the de-vice achieved an energy density of 206 Wh·kg^(-1)and a power density of 2701.05 W·kg^(-1)at a current density of 1 A·g^(-1).Moreover,the flexible zinc-ion hybrid supercapacitor(ZHSC)maintained 89%of its capacitance and nearly 100%Coulombic efficiency after 5500 cycles at 20℃.This work not only advances the development of zinc-ion energy storage devices but al-so establishes a new paradigm for the green and direct utilization of biomass-derived materi-als.展开更多
基金supported by Major Scientific and Technological Innovation Project of Shandong Province(No.2020CXGC011204)Qingdao Natural Science Foundation(No.23-2-1-234-zyyd-jch).
文摘The coexistence of emerging containments,such as antibiotic resistant bacteria(ARB),antibiotic-resistant genes(ARGs)and antibiotics,potentially influence elimination efficiencies in UV light-emitting diode(UV-LED)alone and UV-LED/H_(2)O_(2) system as their complex interactions.Tetracycline(TC)degradation efficiency(kF)correlated closely with its UV molar absorbance(R^(2)=0.831)in UV-LED alone system and with·OH yield(R^(2)=0.999)in UV-LED/H_(2)O_(2) system across studied wavelengths(265,280 and 310 nm).The kF values for intracellular DNA(i-ARGs)also exhibited a high correlation with UV-LED wavelengths in both systems(R^(2)=0.997-0.999).The coexistence of TC and ARB/ARGs resulted in a mutual inhibition of their degradation efficiencies due to competition for photons and·OH,along with the consequent reduction in intracellular ROS within ARB,with their degradation efficiencies exhibiting marked dependence on wavelength in both systems.Notably,the UV-LED/H_(2)O_(2) system at 265 nm effectively achieved the simultaneous removal of TC,ARB and ARGs with minimal energy consumption,and successfully fragmented ARGs.The degradation pathway of TC was analyzed,and the biotoxicity of its degradation intermediates demonstrated the environmental friendliness and safety of UV-LED/H_(2)O_(2) technology.This study elucidated the competitive interactions between antibiotics and ARB/ARGs within UV-LED/H_(2)O_(2) system,providing a promising approach for their simultaneous removal while ensuring energy efficiency.
基金Project supported by the National Natural Sciences Foundation of China(Nos.61334009,61474109,61306050)
文摘High-quality AlN layers with low-density threading dislocations are indispensable for high-efficiency deep ultraviolet light-emitting diodes（UV-LEDs）. In this work, a high-temperature AlN epitaxial layer was grown on sputtered AlN layer（used as nucleation layer, SNL） by a high-yield industrial metalorganic vapor phase epitaxy（MOVPE）. The full width half maximum（FWHM） of the rocking curve shows that the AlN epitaxial layer with SNL has good crystal quality. Furthermore, the relationships between the thickness of SNL and the FWHM values of（002） and（102） peaks were also studied. Finally, utilizing an SNL to enhance the quality of the epitaxial layer, deep UV-LEDs at 282 nm were successfully realized on sapphire substrate by the high-yield industrial MOVPE. The light-output power（LOP） of a deep UV-LED reaches 1.65 mW at 20 mA with external quantum efficiency of 1.87%. In addition, the saturation LOP of the deep UV-LED is 4.31 mW at an injection current of 60 mA. Hence, our studies supply a possible process to grow commercial deep UV-LEDs in high throughput industrial MOVPE, which can increase yield, at lower cost.
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 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sponsored by the National Key Scientific Instrument and Equipment Development Projects of China(Grant No.62027823)the National Natural Science Foun-dation of China(Grant No.61775048).
文摘In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.
基金supported by the National Key R&D Program of China(No.2023YFA1507500)the Nation-al Natural Science Foundation of China(No.52373159)。
文摘Green solvent pretreatment of biomass represents a promising ap-proach for enhancing the econom-ic value of lignocellulosic deriva-tives.In this study,corncob biomass was treated with a diol-based deep eutectic solvent(DES)under mild conditions,facilitating efficient cellulose separation.The extracted cellulose was subsequently used to fabricate cellulose hydrogels in an aqueous zinc chloride solution.The resulting hydrogel exhibited a“water-in-salt”effect due to the high concentration of ZnCl_(2).Leveraging the antifreeze properties of sorbitol,the system demon-strated outstanding low-temperature electrochemical performance,including a broad operat-ing voltage window and an ionic conductivity of 38.4 mS·cm^(-1)at-20℃.At 20℃,the de-vice achieved an energy density of 206 Wh·kg^(-1)and a power density of 2701.05 W·kg^(-1)at a current density of 1 A·g^(-1).Moreover,the flexible zinc-ion hybrid supercapacitor(ZHSC)maintained 89%of its capacitance and nearly 100%Coulombic efficiency after 5500 cycles at 20℃.This work not only advances the development of zinc-ion energy storage devices but al-so establishes a new paradigm for the green and direct utilization of biomass-derived materi-als.
