Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
Rare earth-doped inorganic compounds contribute mostly to the family of persistent luminescent materials due to the versatile energy levels of rare earth ions.One of the key research aims is to match the trap level st...Rare earth-doped inorganic compounds contribute mostly to the family of persistent luminescent materials due to the versatile energy levels of rare earth ions.One of the key research aims is to match the trap level stemming from the doped rare earth ion or intrinsic defects to the electronic structure of the host,and therefore thermoluminescence measurement becomes a radical technology in studying trap depth,which is one of the significant parameters that determine the properties of persistent luminescence and photostimulated luminescence.However,the results of trap depth obtained by different thermoluminescence methods are quite different so that they are not comparable.Herein,we analyzed different thermoluminescence methods,selected and improved the traditional peak position method of T_(m)/500 to be E=(-0.94Inβ+30.09)kT_(m).Only the experimental heating rate(β)is needed additionally,but the accuracy is improved greatly in most cases.This convenient and accurate method will accelerate the discovery of novel rare earth-doped materials.展开更多
对于复杂天气场景图像模糊、低对比度和颜色失真所导致的深度信息预测不准的问题,以往的研究均以标准场景的深度图作为先验信息来对该类场景进行深度估计。然而,这一方式存在先验信息精度较低等问题。对此,提出一个基于多尺度注意力机...对于复杂天气场景图像模糊、低对比度和颜色失真所导致的深度信息预测不准的问题,以往的研究均以标准场景的深度图作为先验信息来对该类场景进行深度估计。然而,这一方式存在先验信息精度较低等问题。对此,提出一个基于多尺度注意力机制的单目深度估计模型TalentDepth,以实现对复杂天气场景的预测。首先,在编码器中融合多尺度注意力机制,在减少计算成本的同时,保留每个通道的信息,提高特征提取的效率和能力。其次,针对图像深度不清晰的问题,基于几何一致性,提出深度区域细化(Depth Region Refinement,DSR)模块,过滤不准确的像素点,以提高深度信息的可靠性。最后,输入图像翻译模型所生成的复杂样本,并计算相应原始图像上的标准损失来指导模型的自监督训练。在NuScence,KITTI和KITTI-C这3个数据集上,相比于基线模型,所提模型对误差和精度均有优化。展开更多
Depth maps play a crucial role in various practical applications such as computer vision,augmented reality,and autonomous driving.How to obtain clear and accurate depth information in video depth estimation is a signi...Depth maps play a crucial role in various practical applications such as computer vision,augmented reality,and autonomous driving.How to obtain clear and accurate depth information in video depth estimation is a significant challenge faced in the field of computer vision.However,existing monocular video depth estimation models tend to produce blurred or inaccurate depth information in regions with object edges and low texture.To address this issue,we propose a monocular depth estimation model architecture guided by semantic segmentation masks,which introduces semantic information into the model to correct the ambiguous depth regions.We have evaluated the proposed method,and experimental results show that our method improves the accuracy of edge depth,demonstrating the effectiveness of our approach.展开更多
自监督单目深度估计受到了国内外研究人员的广泛关注。现有基于深度学习的自监督单目深度估计方法主要采用编码器-解码器结构。然而,这些方法在编码过程中对输入图像进行下采样操作,导致部分图像信息,尤其是图像的边界信息丢失,进而影...自监督单目深度估计受到了国内外研究人员的广泛关注。现有基于深度学习的自监督单目深度估计方法主要采用编码器-解码器结构。然而,这些方法在编码过程中对输入图像进行下采样操作,导致部分图像信息,尤其是图像的边界信息丢失,进而影响深度图的精度。针对上述问题,提出一种基于拉普拉斯金字塔的自监督单目深度估计方法(Self-supervised Monocular Depth Estimation Based on the Laplace Pyramid,LpDepth)。此方法的核心思想是:首先,使用拉普拉斯残差图丰富编码特征,以弥补在下采样过程中丢失的特征信息;其次,在下采样过程中使用最大池化层突显和放大特征信息,使编码器在特征提取过程中更容易地提取到训练模型所需要的特征信息;最后,使用残差模块解决过拟合问题,提高解码器对特征的利用效率。在KITTI和Make3D等数据集上对所提方法进行了测试,同时将其与现有经典方法进行了比较。实验结果证明了所提方法的有效性。展开更多
Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their us...Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their use in wearable devices.To overcome this,recent research by X.Liu et al.presents a compact binocular metalens-based depth perception system that integrates efficient edge detection through an advanced neural network.This system enables accurate,realtime depth mapping even in complex environments,enhancing potential applications in augmented reality,robotics,and autonomous systems.展开更多
Maintaining the s-polarization state of laser beams is important to achieve high modulation depth in a laser-interference-based super-resolution structured illumination microscope(SR-SIM).However,the imperfect optical...Maintaining the s-polarization state of laser beams is important to achieve high modulation depth in a laser-interference-based super-resolution structured illumination microscope(SR-SIM).However,the imperfect optical components can depolarize the laser beams hence degenerating the modulation depth.Here,we first presented a direct measurement method designed to estimate the modulation depth more precisely by shifting illumination patterns with equal phase steps.This measurement method greatly reduces the dependence of modulation depths on the samples,and then developed a polarization optimization method to achieve high modulation depth at all orientations by actively and quantitatively compensating for the additional phase difference using a combination of waveplate and a liquid crystal variable retarder(LCVR).Experimental results demonstrate that our method can achieve illumination patterns with modulation depth higher than 0.94 at three orientations with only one LCVR voltage,which enables isotropic resolution improvement.展开更多
In the dynamic scene of autonomous vehicles,the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation.To solve this problem,we propose an unsupervised monocular depth estima...In the dynamic scene of autonomous vehicles,the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation.To solve this problem,we propose an unsupervised monocular depth estimation model based on edge enhancement,which is specifically aimed at the depth perception challenge in dynamic scenes.The model consists of two core networks:a deep prediction network and a motion estimation network,both of which adopt an encoder-decoder architecture.The depth prediction network is based on the U-Net structure of ResNet18,which is responsible for generating the depth map of the scene.The motion estimation network is based on the U-Net structure of Flow-Net,focusing on the motion estimation of dynamic targets.In the decoding stage of the motion estimation network,we innovatively introduce an edge-enhanced decoder,which integrates a convolutional block attention module(CBAM)in the decoding process to enhance the recognition ability of the edge features of moving objects.In addition,we also designed a strip convolution module to improve the model’s capture efficiency of discrete moving targets.To further improve the performance of the model,we propose a novel edge regularization method based on the Laplace operator,which effectively accelerates the convergence process of themodel.Experimental results on the KITTI and Cityscapes datasets show that compared with the current advanced dynamic unsupervised monocular model,the proposed model has a significant improvement in depth estimation accuracy and convergence speed.Specifically,the rootmean square error(RMSE)is reduced by 4.8%compared with the DepthMotion algorithm,while the training convergence speed is increased by 36%,which shows the superior performance of the model in the depth estimation task in dynamic scenes.展开更多
Coal seam water injection in tunnels is an effective technical measure for preventing coal mine rock bursts.This study used the improved split Hopkinson pressure bar(SHPB)to apply three equal static stresses to water-...Coal seam water injection in tunnels is an effective technical measure for preventing coal mine rock bursts.This study used the improved split Hopkinson pressure bar(SHPB)to apply three equal static stresses to water-saturated coal to simulate the initial stress environment of coal at different depths.Then,dynamic mechanical experiments were conducted on the saturated coal at different depths to investigate the effects of water saturation and depth on the coal samples’dynamic mechanical properties.Under uniaxial compression and without lateral compression,the strength of coal samples decreased to varying degrees in the saturated state;under different depth conditions,the dynamic strength of coal in the saturated state decreased compared with that in the natural state.However,compared with that at 0 m,the reduction in the strength of coal under the saturated condition at 200,400,600,and 800 m was significantly reduced.The findings of this study provide a basic theoretical foundation for the prevention and control of dynamic coal mine disasters.展开更多
Eye depth is an important agronomic trait affecting tubers'appearance,quality,and processing suitability.Hence,cultivating varieties with uniform shapes and shallow eye depth are important goals for potato breedin...Eye depth is an important agronomic trait affecting tubers'appearance,quality,and processing suitability.Hence,cultivating varieties with uniform shapes and shallow eye depth are important goals for potato breeding.In this study,based on the primary mapping of the tuber eyedepth locus using a small primary-segregating population,a large secondary-segregating population with 2100 individuals was used to map the eye-depth locus further.A major quantitative trait locus for eye-depth on chromosome 10 was identified(designated qEyd10.1)using BSAseq and traditional QTL mapping methods.The qEyd10.1 could explain 55.0%of the eye depth phenotypic variation and was further narrowed to a 309.10 kb interval using recombinant analysis.To predict candidate genes,tissue sectioning and RNA-seq of the specific tuber tissues were performed.Genes encoding members of the peroxidase superfamily with likely roles in indole acetic acid regulation were considered the most promising candidates.These results will facilitate marker-assisted selection for the shallow-eye trait in potato breeding and provide a solid basis for eye-depth gene cloning and the analysis of tuber eye-depth regulatory mechanisms.展开更多
During upward horizontal stratified backfill mining,stable backfill is essential for cap and sill pillar recovery.Currently,the primary method for calculating the required strength of backfill is the generalized three...During upward horizontal stratified backfill mining,stable backfill is essential for cap and sill pillar recovery.Currently,the primary method for calculating the required strength of backfill is the generalized three-dimensional(3 D)vertical stress model,which ignores the effect of mine depth,failing to obtain the vertical stress at different positions along stope length.Therefore,this paper develops and validates an improved 3 D model solution through numerical simulation in Rhino-FLAC^(3D),and examines the stress state and stability of backfill under different conditions.The results show that the improved model can accurately calculate the vertical stress at different mine depths and positions along stope length.The error rates between the results of the improved model and numerical simulation are below 4%,indicating high reliability and applicability.The maximum vertical stress(σ_(zz,max))in backfill is positively correlated with the degree of rock-backfill closure,which is enhanced by mine depth and elastic modulus of backfill,while weakened by stope width and inclination,backfill friction angle,and elastic modulus of rock mass.Theσ_(zz,max)reaches its peak when the stope length is 150 m,whileσ_(zz,max)is insensitive to changes in rock-backfill interface parameters.In all cases,the backfill stability can be improved by reducingσ_(zz,max).The results provide theoretical guidance for the backfill strength design and the safe and efficient recovery of ore pillars in deep mining.展开更多
This paper analyzes the text of 3261 clauses of 20 RTAs signed by China,classifies them into 52 policy areas according to the international mainstream HMS method,and assigns them through coding.The clause depth of Ch...This paper analyzes the text of 3261 clauses of 20 RTAs signed by China,classifies them into 52 policy areas according to the international mainstream HMS method,and assigns them through coding.The clause depth of China’s RTAs is measured across three-dimensional systems(policy areas,clauses,and core clauses)and two generations of trade policy areas(WTO+,WTO-X,and all policy areas).It is observed that China’s RTAs exhibit greater depth in Industrial Products,Agricultural Products,TBT,Antidumping,Countervailing,and Investment,while showing comparatively less depth in Fiscal Policy,Innovation Policies,and related areas.展开更多
AIM:To assess visual outcomes and satisfaction of a non-diffractive extended depth of focus(EDOF)intraocular lens(IOL)in individuals with ocular hypertension(OHT)and well-controlled mild glaucoma undergoing cataract s...AIM:To assess visual outcomes and satisfaction of a non-diffractive extended depth of focus(EDOF)intraocular lens(IOL)in individuals with ocular hypertension(OHT)and well-controlled mild glaucoma undergoing cataract surgery.METHODS:An investigator-initiated,single-center,prospective,interventional,noncomparative study conducted in Montreal,Canada.The study enrolled 31 patients(55 eyes)with OHT or mild glaucoma who received a non-diffractive EDOF IOL(Acrysof IQ Vivity).Participants underwent sequential cataract surgery with the Vivity IOL.Follow-up evaluations occurred at 1d,1,and 3mo postoperatively,assessing uncorrected distance,intermediate,and near visual acuity.Questionnaires(QUVID:Questionnaire for visual disturbances and IOLSAT:Intraocular lens satisfaction)were administered pre and post-operatively to measure visual disturbances and spectacle independence in various lighting.Safety parameters included intraocular pressure(IOP),glaucoma medications,spherical equivalence,mean deviation and pattern standard deviation or square root of lost variance on Octopus visual field.RESULTS:At 1 and 3mo postoperatively,significant improvements were observed in uncorrected distance and intermediate visual acuity.Spectacle independence was enhanced for distance and intermediate vision,especially in bright light settings.Spectacle-free intermediate vision was improved even in dim lighting.Visual disturbances,particularly glare symptoms,were reduced,and there was a notable decrease in IOP and glaucoma medication burden at 3mo.There was more hazy vision postoperatively with no impact on visual acuity and visual satisfaction.CONCLUSION:The non-diffractive EDOF lens improves distance and intermediate spectacle-free visual function in patients with OHT and well-controlled glaucoma.The findings highlight significant improvements in visual acuity,reduced glare,enhanced spectacle independence,and improved visual performance in different lighting conditions.展开更多
As shale gas technology has advanced,the horizontal well fracturing model has seen widespread use,leading to substantial improvements in industrial gas output from shale gas wells.Nevertheless,a swift decline in the p...As shale gas technology has advanced,the horizontal well fracturing model has seen widespread use,leading to substantial improvements in industrial gas output from shale gas wells.Nevertheless,a swift decline in the productivity of individual wells remains a challenge that must be addressed throughout the development process.In this study,gas wells with two different wellbore trajectory structures are considered,and the OLGA software is exploited to perform transient calculations on various tubing depth models.The results can be articulated as follows.In terms of flow patterns:for the deep well A1(upward-buckled),slug flow occurs in the Kick-off Point position and above;for the deep well B1(downward-inclined),slug flow only occurs in the horizontal section.Wells with downward-inclined horizontal sections are more prone to liquid accumulation issues.In terms of comparison to conventional wells,it is shown that deep shale gas wells have longer normal production durations and experience liquid accumulation later than conventional wells.With regard to optimal tubing placement:for well A1(upward-buckled),it is recommended to place tubing at the Kick-off Point position;for well B1(downward-inclined),it is recommended to place tubing at the lower heel of the horizontal section.Finally,in terms of production performance:well A1(upward-buckled)outperforms well B1(downward-inclined)in terms of production and fluid accumulation.In particular,the deep well A1 is 1.94 times more productive and 1.3 times longer to produce than conventional wells.Deep well B1 is 1.87 times more productive and 1.34 times longer than conventional wells.展开更多
Understanding water uptake depth and its relationship with functional traits offers valuable insights into resource-use partitioning among coexisting tree species as well as forest responses to drought.However,knowled...Understanding water uptake depth and its relationship with functional traits offers valuable insights into resource-use partitioning among coexisting tree species as well as forest responses to drought.However,knowledge about water uptake patterns in vertical soil layers,especially among increasingly widespread secondary forest tree species,remains limited.In this study,we investigated interspecific and seasonal variations in water uptake depth among seven coexisting tree species over a 2-year period in a warm-temperate secondary forest in central Japan.We also analyzed the relationships of water uptake depth with tree height and functional traits,including specific leaf area(SLA),leaf dry matter content(LDMC),leaf nitrogen(N)content,and wood density(WD),to discern resource-use and-acquisition strategies.Results revealed that taller trees,especially when soil water is scarce,tend to access deeper soil water sources,indicating that water source partitioning is correlated with tree height.This interspecific and temporal variation in water sources likely stratifies trees to facilitate coexistence within the forest.Water uptake depth was primarily associated with WD and LDMC:trees absorbing more water from shallow soils during dry conditions exhibited lower WD and LDMC,indicating a proactive resource-use strategy.Conversely,SLA and leaf N content were orthogonal to water uptake depth,suggesting that strategies for acquiring belowground and aboveground resources may differ.Considering the alternation of tree species composition during secondary forest succession,our study highlights the importance of further data collection regarding root water uptake depth along successional stages to understand dynamic shifts in water uptake sources.展开更多
Normal mode extraction has attracted extensive attention over the past few decades due to its practical value in enhancing the performance of underwater acoustic signal processing.Singular value decomposition(SVD)is a...Normal mode extraction has attracted extensive attention over the past few decades due to its practical value in enhancing the performance of underwater acoustic signal processing.Singular value decomposition(SVD)is an effective method to extract modal depth functions using vertical line arrays(VLA),particularly in scenarios when no prior environment information is available.However,the SVD method requires rigorous orthogonality conditions,and its performance severely degenerates in the presence of mode degeneracy.Consequently,the SVD approach is often not feasible in practical scenarios.This paper proposes a full rank decomposition(FRD)method to address these issues.Compared to the SVD method,the FRD method has three distinct advantages:1)the conditions that the FRD method requires are much easier to be fulfilled in practical scenarios;2)both modal depth functions and wavenumbers can be simultaneously extracted via the FRD method;3)the FRD method is not affected by the phenomenon of mode degeneracy.Numerical simulations are conducted in two types of waveguides to verify the FRD method.The impacts of environment configurations and noise levels on the precision of the extracted modal depth functions and wavenumbers are also investigated through simulation.展开更多
Object detection in occluded environments remains a core challenge in computer vision(CV),especially in domains such as autonomous driving and robotics.While Convolutional Neural Network(CNN)-based twodimensional(2D)a...Object detection in occluded environments remains a core challenge in computer vision(CV),especially in domains such as autonomous driving and robotics.While Convolutional Neural Network(CNN)-based twodimensional(2D)and three-dimensional(3D)object detection methods havemade significant progress,they often fall short under severe occlusion due to depth ambiguities in 2D imagery and the high cost and deployment limitations of 3D sensors such as Light Detection and Ranging(LiDAR).This paper presents a comparative review of recent 2D and 3D detection models,focusing on their occlusion-handling capabilities and the impact of sensor modalities such as stereo vision,Time-of-Flight(ToF)cameras,and LiDAR.In this context,we introduce FuDensityNet,our multimodal occlusion-aware detection framework that combines Red-Green-Blue(RGB)images and LiDAR data to enhance detection performance.As a forward-looking direction,we propose a monocular depth-estimation extension to FuDensityNet,aimed at replacing expensive 3D sensors with a more scalable CNN-based pipeline.Although this enhancement is not experimentally evaluated in this manuscript,we describe its conceptual design and potential for future implementation.展开更多
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金Project supported by the National Natural Science Foundation of China(52372134,12274023)the Fundamental Re search Funds for the Central Universities(FRF-EYIT-23-04)。
文摘Rare earth-doped inorganic compounds contribute mostly to the family of persistent luminescent materials due to the versatile energy levels of rare earth ions.One of the key research aims is to match the trap level stemming from the doped rare earth ion or intrinsic defects to the electronic structure of the host,and therefore thermoluminescence measurement becomes a radical technology in studying trap depth,which is one of the significant parameters that determine the properties of persistent luminescence and photostimulated luminescence.However,the results of trap depth obtained by different thermoluminescence methods are quite different so that they are not comparable.Herein,we analyzed different thermoluminescence methods,selected and improved the traditional peak position method of T_(m)/500 to be E=(-0.94Inβ+30.09)kT_(m).Only the experimental heating rate(β)is needed additionally,but the accuracy is improved greatly in most cases.This convenient and accurate method will accelerate the discovery of novel rare earth-doped materials.
文摘对于复杂天气场景图像模糊、低对比度和颜色失真所导致的深度信息预测不准的问题,以往的研究均以标准场景的深度图作为先验信息来对该类场景进行深度估计。然而,这一方式存在先验信息精度较低等问题。对此,提出一个基于多尺度注意力机制的单目深度估计模型TalentDepth,以实现对复杂天气场景的预测。首先,在编码器中融合多尺度注意力机制,在减少计算成本的同时,保留每个通道的信息,提高特征提取的效率和能力。其次,针对图像深度不清晰的问题,基于几何一致性,提出深度区域细化(Depth Region Refinement,DSR)模块,过滤不准确的像素点,以提高深度信息的可靠性。最后,输入图像翻译模型所生成的复杂样本,并计算相应原始图像上的标准损失来指导模型的自监督训练。在NuScence,KITTI和KITTI-C这3个数据集上,相比于基线模型,所提模型对误差和精度均有优化。
文摘Depth maps play a crucial role in various practical applications such as computer vision,augmented reality,and autonomous driving.How to obtain clear and accurate depth information in video depth estimation is a significant challenge faced in the field of computer vision.However,existing monocular video depth estimation models tend to produce blurred or inaccurate depth information in regions with object edges and low texture.To address this issue,we propose a monocular depth estimation model architecture guided by semantic segmentation masks,which introduces semantic information into the model to correct the ambiguous depth regions.We have evaluated the proposed method,and experimental results show that our method improves the accuracy of edge depth,demonstrating the effectiveness of our approach.
文摘自监督单目深度估计受到了国内外研究人员的广泛关注。现有基于深度学习的自监督单目深度估计方法主要采用编码器-解码器结构。然而,这些方法在编码过程中对输入图像进行下采样操作,导致部分图像信息,尤其是图像的边界信息丢失,进而影响深度图的精度。针对上述问题,提出一种基于拉普拉斯金字塔的自监督单目深度估计方法(Self-supervised Monocular Depth Estimation Based on the Laplace Pyramid,LpDepth)。此方法的核心思想是:首先,使用拉普拉斯残差图丰富编码特征,以弥补在下采样过程中丢失的特征信息;其次,在下采样过程中使用最大池化层突显和放大特征信息,使编码器在特征提取过程中更容易地提取到训练模型所需要的特征信息;最后,使用残差模块解决过拟合问题,提高解码器对特征的利用效率。在KITTI和Make3D等数据集上对所提方法进行了测试,同时将其与现有经典方法进行了比较。实验结果证明了所提方法的有效性。
基金financially supported by the POSCO-POSTECH-RIST Convergence Research Center program funded by POSCOthe National Research Foundation (NRF) grants (RS-2024-00462912, RS-2024-00416272, RS-2024-00337012, RS-2024-00408446) funded by the Ministry of Science and ICT (MSIT) of the Korean government+2 种基金the Korea Evaluation Institute of Industrial Technology (KEIT) grant (No. 1415185027/20019169, Alchemist project) funded by the Ministry of Trade, Industry and Energy (MOTIE) of the Korean governmentthe Soseon Science fellowship funded by Community Chest of Koreathe NRF PhD fellowship (RS-2023-00275565) funded by the Ministry of Education (MOE) of the Korean government。
文摘Spatial computing and augmented reality are advancing rapidly,with the goal of seamlessly blending virtual and physical worlds.However,traditional depth-sensing systems are bulky and energy-intensive,limiting their use in wearable devices.To overcome this,recent research by X.Liu et al.presents a compact binocular metalens-based depth perception system that integrates efficient edge detection through an advanced neural network.This system enables accurate,realtime depth mapping even in complex environments,enhancing potential applications in augmented reality,robotics,and autonomous systems.
基金supported by the National Natural Science Foundation of China[Grant Nos.62205367 and 62141506]the Suzhou Basic Research Pilot Project[Grant Nos.SSD2023006 and SJC2021013]the National Key Research and Development Program of China[Grant No.2023YFF1205700].
文摘Maintaining the s-polarization state of laser beams is important to achieve high modulation depth in a laser-interference-based super-resolution structured illumination microscope(SR-SIM).However,the imperfect optical components can depolarize the laser beams hence degenerating the modulation depth.Here,we first presented a direct measurement method designed to estimate the modulation depth more precisely by shifting illumination patterns with equal phase steps.This measurement method greatly reduces the dependence of modulation depths on the samples,and then developed a polarization optimization method to achieve high modulation depth at all orientations by actively and quantitatively compensating for the additional phase difference using a combination of waveplate and a liquid crystal variable retarder(LCVR).Experimental results demonstrate that our method can achieve illumination patterns with modulation depth higher than 0.94 at three orientations with only one LCVR voltage,which enables isotropic resolution improvement.
基金funded by the Yangtze River Delta Science and Technology Innovation Community Joint Research Project(2023CSJGG1600)the Natural Science Foundation of Anhui Province(2208085MF173)Wuhu“ChiZhu Light”Major Science and Technology Project(2023ZD01,2023ZD03).
文摘In the dynamic scene of autonomous vehicles,the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation.To solve this problem,we propose an unsupervised monocular depth estimation model based on edge enhancement,which is specifically aimed at the depth perception challenge in dynamic scenes.The model consists of two core networks:a deep prediction network and a motion estimation network,both of which adopt an encoder-decoder architecture.The depth prediction network is based on the U-Net structure of ResNet18,which is responsible for generating the depth map of the scene.The motion estimation network is based on the U-Net structure of Flow-Net,focusing on the motion estimation of dynamic targets.In the decoding stage of the motion estimation network,we innovatively introduce an edge-enhanced decoder,which integrates a convolutional block attention module(CBAM)in the decoding process to enhance the recognition ability of the edge features of moving objects.In addition,we also designed a strip convolution module to improve the model’s capture efficiency of discrete moving targets.To further improve the performance of the model,we propose a novel edge regularization method based on the Laplace operator,which effectively accelerates the convergence process of themodel.Experimental results on the KITTI and Cityscapes datasets show that compared with the current advanced dynamic unsupervised monocular model,the proposed model has a significant improvement in depth estimation accuracy and convergence speed.Specifically,the rootmean square error(RMSE)is reduced by 4.8%compared with the DepthMotion algorithm,while the training convergence speed is increased by 36%,which shows the superior performance of the model in the depth estimation task in dynamic scenes.
基金Projects(52225403,52074112)supported by the National Natural Science Foundation of ChinaProject(2022CFD009)supported by the Hubei Natural Science Foundation Innovation and Development Joint Fund Key Project,China+2 种基金Project(SDGZK2423)supported by the State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering,ChinaProject(HJZKYBKT2024111)supported by the Xiangyang Federation of Social Sciences“Hanjiang Think Tank”Project,ChinaProject supported by the Hubei Superior and Distinctive Discipline Group of“New Energy Vehicle and Smart Transportation”,China。
文摘Coal seam water injection in tunnels is an effective technical measure for preventing coal mine rock bursts.This study used the improved split Hopkinson pressure bar(SHPB)to apply three equal static stresses to water-saturated coal to simulate the initial stress environment of coal at different depths.Then,dynamic mechanical experiments were conducted on the saturated coal at different depths to investigate the effects of water saturation and depth on the coal samples’dynamic mechanical properties.Under uniaxial compression and without lateral compression,the strength of coal samples decreased to varying degrees in the saturated state;under different depth conditions,the dynamic strength of coal in the saturated state decreased compared with that in the natural state.However,compared with that at 0 m,the reduction in the strength of coal under the saturated condition at 200,400,600,and 800 m was significantly reduced.The findings of this study provide a basic theoretical foundation for the prevention and control of dynamic coal mine disasters.
基金funded by the National Natural Science Foundation of China(Grant No.31801421)the Chinese Academy of Agricultural Sciences Innovation Project(Grant No.CAAS-ASTIPIVFCAAS).
文摘Eye depth is an important agronomic trait affecting tubers'appearance,quality,and processing suitability.Hence,cultivating varieties with uniform shapes and shallow eye depth are important goals for potato breeding.In this study,based on the primary mapping of the tuber eyedepth locus using a small primary-segregating population,a large secondary-segregating population with 2100 individuals was used to map the eye-depth locus further.A major quantitative trait locus for eye-depth on chromosome 10 was identified(designated qEyd10.1)using BSAseq and traditional QTL mapping methods.The qEyd10.1 could explain 55.0%of the eye depth phenotypic variation and was further narrowed to a 309.10 kb interval using recombinant analysis.To predict candidate genes,tissue sectioning and RNA-seq of the specific tuber tissues were performed.Genes encoding members of the peroxidase superfamily with likely roles in indole acetic acid regulation were considered the most promising candidates.These results will facilitate marker-assisted selection for the shallow-eye trait in potato breeding and provide a solid basis for eye-depth gene cloning and the analysis of tuber eye-depth regulatory mechanisms.
基金Project(2024ZD1003704)supported by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project,ChinaProjects(51834001,52130404)supported by the National Natural Science Foundation of China。
文摘During upward horizontal stratified backfill mining,stable backfill is essential for cap and sill pillar recovery.Currently,the primary method for calculating the required strength of backfill is the generalized three-dimensional(3 D)vertical stress model,which ignores the effect of mine depth,failing to obtain the vertical stress at different positions along stope length.Therefore,this paper develops and validates an improved 3 D model solution through numerical simulation in Rhino-FLAC^(3D),and examines the stress state and stability of backfill under different conditions.The results show that the improved model can accurately calculate the vertical stress at different mine depths and positions along stope length.The error rates between the results of the improved model and numerical simulation are below 4%,indicating high reliability and applicability.The maximum vertical stress(σ_(zz,max))in backfill is positively correlated with the degree of rock-backfill closure,which is enhanced by mine depth and elastic modulus of backfill,while weakened by stope width and inclination,backfill friction angle,and elastic modulus of rock mass.Theσ_(zz,max)reaches its peak when the stope length is 150 m,whileσ_(zz,max)is insensitive to changes in rock-backfill interface parameters.In all cases,the backfill stability can be improved by reducingσ_(zz,max).The results provide theoretical guidance for the backfill strength design and the safe and efficient recovery of ore pillars in deep mining.
基金General Project of Beijing Social Science Foundation,“Research on the Internal and External Strategic Alignment of Regional Trade Agreements and the High-Quality Construction of China(Beijing)Pilot Free Trade Zone”(Project No.:21GLB021)。
文摘This paper analyzes the text of 3261 clauses of 20 RTAs signed by China,classifies them into 52 policy areas according to the international mainstream HMS method,and assigns them through coding.The clause depth of China’s RTAs is measured across three-dimensional systems(policy areas,clauses,and core clauses)and two generations of trade policy areas(WTO+,WTO-X,and all policy areas).It is observed that China’s RTAs exhibit greater depth in Industrial Products,Agricultural Products,TBT,Antidumping,Countervailing,and Investment,while showing comparatively less depth in Fiscal Policy,Innovation Policies,and related areas.
文摘AIM:To assess visual outcomes and satisfaction of a non-diffractive extended depth of focus(EDOF)intraocular lens(IOL)in individuals with ocular hypertension(OHT)and well-controlled mild glaucoma undergoing cataract surgery.METHODS:An investigator-initiated,single-center,prospective,interventional,noncomparative study conducted in Montreal,Canada.The study enrolled 31 patients(55 eyes)with OHT or mild glaucoma who received a non-diffractive EDOF IOL(Acrysof IQ Vivity).Participants underwent sequential cataract surgery with the Vivity IOL.Follow-up evaluations occurred at 1d,1,and 3mo postoperatively,assessing uncorrected distance,intermediate,and near visual acuity.Questionnaires(QUVID:Questionnaire for visual disturbances and IOLSAT:Intraocular lens satisfaction)were administered pre and post-operatively to measure visual disturbances and spectacle independence in various lighting.Safety parameters included intraocular pressure(IOP),glaucoma medications,spherical equivalence,mean deviation and pattern standard deviation or square root of lost variance on Octopus visual field.RESULTS:At 1 and 3mo postoperatively,significant improvements were observed in uncorrected distance and intermediate visual acuity.Spectacle independence was enhanced for distance and intermediate vision,especially in bright light settings.Spectacle-free intermediate vision was improved even in dim lighting.Visual disturbances,particularly glare symptoms,were reduced,and there was a notable decrease in IOP and glaucoma medication burden at 3mo.There was more hazy vision postoperatively with no impact on visual acuity and visual satisfaction.CONCLUSION:The non-diffractive EDOF lens improves distance and intermediate spectacle-free visual function in patients with OHT and well-controlled glaucoma.The findings highlight significant improvements in visual acuity,reduced glare,enhanced spectacle independence,and improved visual performance in different lighting conditions.
文摘As shale gas technology has advanced,the horizontal well fracturing model has seen widespread use,leading to substantial improvements in industrial gas output from shale gas wells.Nevertheless,a swift decline in the productivity of individual wells remains a challenge that must be addressed throughout the development process.In this study,gas wells with two different wellbore trajectory structures are considered,and the OLGA software is exploited to perform transient calculations on various tubing depth models.The results can be articulated as follows.In terms of flow patterns:for the deep well A1(upward-buckled),slug flow occurs in the Kick-off Point position and above;for the deep well B1(downward-inclined),slug flow only occurs in the horizontal section.Wells with downward-inclined horizontal sections are more prone to liquid accumulation issues.In terms of comparison to conventional wells,it is shown that deep shale gas wells have longer normal production durations and experience liquid accumulation later than conventional wells.With regard to optimal tubing placement:for well A1(upward-buckled),it is recommended to place tubing at the Kick-off Point position;for well B1(downward-inclined),it is recommended to place tubing at the lower heel of the horizontal section.Finally,in terms of production performance:well A1(upward-buckled)outperforms well B1(downward-inclined)in terms of production and fluid accumulation.In particular,the deep well A1 is 1.94 times more productive and 1.3 times longer to produce than conventional wells.Deep well B1 is 1.87 times more productive and 1.34 times longer than conventional wells.
基金supported by Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research[Grant No.19H02992]Grant for the Environmental Research Projects,the Sumitomo Foundation[Grant No.2230116].
文摘Understanding water uptake depth and its relationship with functional traits offers valuable insights into resource-use partitioning among coexisting tree species as well as forest responses to drought.However,knowledge about water uptake patterns in vertical soil layers,especially among increasingly widespread secondary forest tree species,remains limited.In this study,we investigated interspecific and seasonal variations in water uptake depth among seven coexisting tree species over a 2-year period in a warm-temperate secondary forest in central Japan.We also analyzed the relationships of water uptake depth with tree height and functional traits,including specific leaf area(SLA),leaf dry matter content(LDMC),leaf nitrogen(N)content,and wood density(WD),to discern resource-use and-acquisition strategies.Results revealed that taller trees,especially when soil water is scarce,tend to access deeper soil water sources,indicating that water source partitioning is correlated with tree height.This interspecific and temporal variation in water sources likely stratifies trees to facilitate coexistence within the forest.Water uptake depth was primarily associated with WD and LDMC:trees absorbing more water from shallow soils during dry conditions exhibited lower WD and LDMC,indicating a proactive resource-use strategy.Conversely,SLA and leaf N content were orthogonal to water uptake depth,suggesting that strategies for acquiring belowground and aboveground resources may differ.Considering the alternation of tree species composition during secondary forest succession,our study highlights the importance of further data collection regarding root water uptake depth along successional stages to understand dynamic shifts in water uptake sources.
基金supported by the National Natural Science Foundation of China(Nos.12304504,12304506 and U22 A2012)the Youth Innovation Promotion Association,Chinese Academy of Sciences(No.2021023)+1 种基金the Strategy Priority Research Program(Category B)of Chinese Academy of Sciences(Nos.XDB0700100 and XDB0700000)the Natural Science Foundation of Tianjin(No.22JCYBJC00070).
文摘Normal mode extraction has attracted extensive attention over the past few decades due to its practical value in enhancing the performance of underwater acoustic signal processing.Singular value decomposition(SVD)is an effective method to extract modal depth functions using vertical line arrays(VLA),particularly in scenarios when no prior environment information is available.However,the SVD method requires rigorous orthogonality conditions,and its performance severely degenerates in the presence of mode degeneracy.Consequently,the SVD approach is often not feasible in practical scenarios.This paper proposes a full rank decomposition(FRD)method to address these issues.Compared to the SVD method,the FRD method has three distinct advantages:1)the conditions that the FRD method requires are much easier to be fulfilled in practical scenarios;2)both modal depth functions and wavenumbers can be simultaneously extracted via the FRD method;3)the FRD method is not affected by the phenomenon of mode degeneracy.Numerical simulations are conducted in two types of waveguides to verify the FRD method.The impacts of environment configurations and noise levels on the precision of the extracted modal depth functions and wavenumbers are also investigated through simulation.
文摘Object detection in occluded environments remains a core challenge in computer vision(CV),especially in domains such as autonomous driving and robotics.While Convolutional Neural Network(CNN)-based twodimensional(2D)and three-dimensional(3D)object detection methods havemade significant progress,they often fall short under severe occlusion due to depth ambiguities in 2D imagery and the high cost and deployment limitations of 3D sensors such as Light Detection and Ranging(LiDAR).This paper presents a comparative review of recent 2D and 3D detection models,focusing on their occlusion-handling capabilities and the impact of sensor modalities such as stereo vision,Time-of-Flight(ToF)cameras,and LiDAR.In this context,we introduce FuDensityNet,our multimodal occlusion-aware detection framework that combines Red-Green-Blue(RGB)images and LiDAR data to enhance detection performance.As a forward-looking direction,we propose a monocular depth-estimation extension to FuDensityNet,aimed at replacing expensive 3D sensors with a more scalable CNN-based pipeline.Although this enhancement is not experimentally evaluated in this manuscript,we describe its conceptual design and potential for future implementation.
