Yogyakarta was struck by a devastating Mw6.3 earthquake,which intensified awareness about the seismic hazards in the region.This study investigates the kinematic slip rate and seismogenic depth of the northern segment...Yogyakarta was struck by a devastating Mw6.3 earthquake,which intensified awareness about the seismic hazards in the region.This study investigates the kinematic slip rate and seismogenic depth of the northern segment of Opak Fault and an unmapped fault known as Ngalang Fault in Yogyakarta,utilizing Global Navigation Satellite System(GNSS)data collected between 2019 and 2023.By deploying a network of 12 GNSS stations alongside continuous observations from the InaCORS network,we perfo rmed a detailed geodetic analysis to discern current defo rmation patterns.To quantify the slip rate,we established a frame of reference using the Sundaland Block's rotational parameters and applied the Euler pole angular velocity to transform daily GNSS solutions acco rdingly.The findings reveal significant left-lateral strike-slip motion in the northern segment of Opak Fault,with a slip rate averaging 3 mm/yr and a locking depth of 2.1 km in Northern Segment,whereas the slip rate averages 1.1 mm/yr and the locking depth is estimated at 1 km in the Ngalang Fault,indicating active geological movements that may influence future seismicity.展开更多
The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors,due to the limited capabilities of sensors,which also produce...The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors,due to the limited capabilities of sensors,which also produce poor computer vision results.The common image denoising techniques tend to remove significant image details and also remove noise,provided they are based on space and frequency filtering.The updated framework presented in this paper is a novel denoising model that makes use of Boruta-driven feature selection using a Long Short-Term Memory Autoencoder(LSTMAE).The Boruta algorithm identifies the most useful depth features that are used to maximize the spatial structure integrity and reduce redundancy.An LSTMAE is then used to process these selected features and model depth pixel sequences to generate robust,noise-resistant representations.The system uses the encoder to encode the input data into a latent space that has been compressed before it is decoded to retrieve the clean image.Experiments on a benchmark data set show that the suggested technique attains a PSNR of 45 dB and an SSIM of 0.90,which is 10 dB higher than the performance of conventional convolutional autoencoders and 15 times higher than that of the wavelet-based models.Moreover,the feature selection step will decrease the input dimensionality by 40%,resulting in a 37.5%reduction in training time and a real-time inference rate of 200 FPS.Boruta-LSTMAE framework,therefore,offers a highly efficient and scalable system for depth image denoising,with a high potential to be applied to close-range 3D systems,such as robotic manipulation and gesture-based interfaces.展开更多
This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,suc...This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,such as adaptive boosting(AdaBoost),categorical boosting(CatBoost),gradient boosting regressor(GBR),hist gradient boosting regressor(HistGBR),and extreme gradient boosting(XGBoost),were developed and optimized using 729 high-quality dataset points incorporating seven input parameters,including cement,CO_(2),exposure time,water-binder ratio,fly ash,curing time,and compressive strength.Several performance evaluation metrics were used to compare the models.The GBR model emerged as the best-performing model,based on high coefficient of determination(R^(2))values and balanced error metrics across both validation and testing datasets.While all models performed exceptionally well on the training data,GBR demonstrated superior generalization capability,with R^(2) values of 0.9438 on the validation set and 0.9310 on the testing set.Furthermore,its low mean squared error(MSE),root mean square error(RMSE),mean absolute error(MAE),and median absolute error(MdAE)confirmed its robustness and accuracy.Moreover,shapley additive explanations(SHAP)analysis enhanced the interpretability of predictions,highlighting the curing time and exposure time as the most critical drivers of carbonation depth.展开更多
High-capacity O3-type layered NiFeMn-based oxides are promising cathodes for sodium-ion batteries,though their practical deployment is constrained by the inherent limitations of Fe redox chemistry.Traditional designs ...High-capacity O3-type layered NiFeMn-based oxides are promising cathodes for sodium-ion batteries,though their practical deployment is constrained by the inherent limitations of Fe redox chemistry.Traditional designs generally enforcing stoichiometric symmetry(Ni=Mn)yield low Fe redox activity.Herein,we propose a valence engineering strategy that breaks conventional Ni/Mn stoichiometry to reconfigure Fe's local chemical environment and unlock unprecedented redox depth.Density functional theory(DFT)calculations reveal that the designed NaNi_(0.35)Fe_(0.225)Mn_(0.425)O_(2)cathode exhibits a reduced Bader charge on Fe(1.598 vs.1.638 in NaNi_(1/3)Fe_(1/3)Mn_(1/3)O_(2))and elevated Fe 3d orbital energy,signifying enhanced Fe redox activity.This configuration enables an exceptional Fe^(2.60+)/Fe^(3.88+)redox(1.28 e~-per Fe),delivering a reversible capacity of184.3 mAh g^(-1)within 2-4.2 V at 0.2 C,markedly exceeding the benchmark NaNi_(1/3)Fe_(1/3)Mn_(1/3)O_(2)(161.3 mAh g^(-1))with low reaction depth of Fe^(3.01+)/Fe^(3.61+).The intensified cationic redox reaction enables an ultrahigh energy density of 596 Whkg-1.The NaNi_(0.35)Fe_(0.225)Mn_(0.425)O_(2)cathode demonstrates robust performance over a broad temperature range from-15℃to 60℃.In situ and ex situ characterizations unveil a reversible O3■P3■OP2 phase transition with minimal volume change(1.88%)that circumvents detrimental deleterious O'3 intermediates and intragranular cracking.This work establishes valence engineering as a paradigm to consolidate cationic redox reaction in high-energy layered sodium oxide cathodes.展开更多
The transient phenomena of re-oxidation and slag entrapment occurring in the tundish during the ladle change-over process have been proven detrimental to clean steel production.Therefore,an unsteady three-phase turbul...The transient phenomena of re-oxidation and slag entrapment occurring in the tundish during the ladle change-over process have been proven detrimental to clean steel production.Therefore,an unsteady three-phase turbulence model,coupling velocity,temperature,and phase field was established to study the effect of the ladle shroud immersion depth on the slag eye formation,slag entrainment,slag dragging,air dragging,and flow characteristics during the ladle change-over process of a two-strand tundish.The results showed that reducing the immersion depth decreases the high-velocity region area under the slag layer in the quasi-steady process.During the emptying stage,as the molten bath level gradually decreases,the outlet temperature exhibits a trend of initially decreasing and subsequently increasing across all three shroud immersion depths.However,under a 210 mm shroud immersion depth,molten slag and air are dragged into the shroud,forming slag droplets and causing significant fluctuations,with a maximum scalar velocity of 0.0764 m/s at the monitoring point.In the filling stage,air and molten slag are dragged into the molten bath,forming bubbles and slag droplets at an immersion depth of 210 mm.Bubbles are observed within the molten slag layer,which can readily cause an emulsification phenomenon,making it easier to be dragged as slag droplets.Additionally,the slag eye area measured under 210 mm immersion depth at 45 s is 0.303 m^(2),while the maximum scalar velocity of 2.4259 m/s is detected at 12 s.At an immersion depth of 360 mm,the average area of the slag eye is minimized to 0.06268 m2,with corresponding variances of 0.006753,representing the optimal immersion depth.展开更多
Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters...Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters to estimate source depth accurately.Unlike traditional matched field processing(MFP)and matched mode processing(MMP),the proposed approach can estimate source depth directly from the data received by sensors without requiring complete environmental information.Firstly,the broadband Stokes parameters(BSP)are established using the normal mode theory.Then the nonstationary phase approximation is used to simplify the theoretical derivation,which is necessary when dealing with broadband integrals.Additionally,range terms of the BSP are eliminated by normalization.By analyzing the depth distribution of the normalized broadband Stokes parameters(NBSP),it is found that the NBSP exhibit extreme values at the source depth,which can be used for source depth estimation.So the proposed depth estimation method is based on searching the peaks of the NBSP.Simulations show that this method is effective in relatively simple shallow water environments.Finally,the effect of source range,frequency bandwidth,sound speed profile(SSP),water depth,and signal-to-noise ratio(SNR)are studied.The findings indicate that the proposed method can accurately estimate the source depth when the SNR is greater than-5 d B and does not need to consider model mismatch issues.Additionally,variations in environmental parameters have minimal impact on estimation accuracy.Compared to MFP,the proposed method requires a higher SNR,but demonstrates superior robustness against fluctuations in environmental parameters.展开更多
Objective:To explore the relationship between anesthetic depth and surgical stress response in minimally invasive cardiothoracic surgery.Methods:A total of 89 patients who underwent thoracoscopic minimally invasive ca...Objective:To explore the relationship between anesthetic depth and surgical stress response in minimally invasive cardiothoracic surgery.Methods:A total of 89 patients who underwent thoracoscopic minimally invasive cardiothoracic surgery in our hospital from June 2024 to December 2024 were selected as the research objects.They were divided into the light anesthesia group(n=45)and the deep anesthesia group(n=44).The vital signs at different intraoperative nodes and perioperative stress status of the two groups were compared.Results:Before lesion resection and after surgery,the mean arterial pressure and heart rate of the deep anesthesia group were lower than those of the light anesthesia group,with statistically significant differences.Conclusion:In thoracoscopic minimally invasive cardiothoracic surgery,deep anesthesia can effectively control the patient’s surgical stress response,but the postoperative awakening time is longer;patients under light anesthesia have a shorter awakening time,but the intraoperative stress response is obvious.展开更多
Compared to monocular depth estimation,multi-view depth estimation often yields more accurate results.However,traditional multi-view depth estimation methods often fail to leverage semantic information fully and strug...Compared to monocular depth estimation,multi-view depth estimation often yields more accurate results.However,traditional multi-view depth estimation methods often fail to leverage semantic information fully and struggle to effectively fuse information from multiple views,leading to suboptimal prediction performance in challenging scenarios such as texture-less regions and reflective surfaces.To address these limitations,we present MVI-Depth,a novel framework with two core innovations:(1)a Semantic Fusion Module(SFM)that establishes semantic correspondence,and(2)a Depth Updating Module(DUM)enabling iterative depth refinement.Specifically,MVI-Depth initially establishes a main view representation that integrates single-view depth,depth features,and semantic features.Subsequent feature extraction from neighbouring views enables the construction of the original cost volume.Recognising the inherent limitations of direct cost volume utilisation in complex scenes,the proposed SFM constructs an aligned semantic cost volume to utilise the complementarity between semantic and depth information,forming an improved final cost volume.The final cost volume is updated through the proposed DUM to achieve iterative depth optimisation.Comprehensive evaluations demonstrate that MVI-Depth achieves superior performance across all standard metrics on both ScanNet and KITTI benchmarks,outperforming existing methods.Additional experiments on the 7-Scenes dataset further confirm the framework's robust generalisation capabilities in diverse environments.展开更多
Accurately estimating depth from underwater monocular images is essential for the target tracking task of unmanned underwater vehicles.This work proposes a method based on the Lpg-Lap Unet architecture.First,the Unet ...Accurately estimating depth from underwater monocular images is essential for the target tracking task of unmanned underwater vehicles.This work proposes a method based on the Lpg-Lap Unet architecture.First,the Unet architecture integrates Laplacian pyramid depth residuals and Sobel operators to improve the boundary details in depth images,which may suffer from the feature loss caused by upsampling and the blurriness of underwater images.Multiscale local planar guidance layers then fully exploit the intermediate depth features,and a comprehensive loss function ensures robustness and accuracy.Experimental results on benchmarks demonstrate the effectiveness of Lpg-Lap Unet and its superior performance over state-of-the-art models.An underwater target tracking system is then designed to further validate its real-time capabilities in the AirSim simulation platform.展开更多
Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observat...Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observations in turbid coastal waters.In this paper,we developed a physicsenhanced deep neural network to estimate bathymetry of highly turbid waters of the Changjiang(Yangtze)River estuary from dual-polarized synthetic aperture radar(SAR)images.Sentinel-1A/B SAR images with a spatial resolution of 20 m×22 m were collected and matched with water depth data from nautical charts during 2017-2023.For the input parameters of the model,in addition to the normalized radar backscatter cross section(NRCS)at single polarization and incidence angle,the impacts of both polarimetric characteristics and physical environmental factors on model performance were discussed in detail.Results of feature importance analysis and sensitivity experiments indicate that the polarization ratio and NRCS after removing the influence of background sea surface wind field make significant contributions to the bathymetry retrieval model.The root mean square error(RMSE)of SAR derived water depth decreases from 1.44 to 0.78 m within 0-30-m depth,and the mean relative error(MRE)is reduced from 15.6%to 8.6%.Compared with other machine learning models such as ResNet,XGBoost,and Random Forest,the MRE is reduced by 3.9%,5.7%,and 7.4%,respectively.The spatial distribution of SAR derived water depth also exhibits a high degree of consistency with observations,demonstrating the great potential of the model in estimating the depth of turbid shallow waters.展开更多
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
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
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
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
Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and sub...Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.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
基金funded by the Department of Geodetic Engineering,Faculty of Engineering,Universitas Gadjah Mada。
文摘Yogyakarta was struck by a devastating Mw6.3 earthquake,which intensified awareness about the seismic hazards in the region.This study investigates the kinematic slip rate and seismogenic depth of the northern segment of Opak Fault and an unmapped fault known as Ngalang Fault in Yogyakarta,utilizing Global Navigation Satellite System(GNSS)data collected between 2019 and 2023.By deploying a network of 12 GNSS stations alongside continuous observations from the InaCORS network,we perfo rmed a detailed geodetic analysis to discern current defo rmation patterns.To quantify the slip rate,we established a frame of reference using the Sundaland Block's rotational parameters and applied the Euler pole angular velocity to transform daily GNSS solutions acco rdingly.The findings reveal significant left-lateral strike-slip motion in the northern segment of Opak Fault,with a slip rate averaging 3 mm/yr and a locking depth of 2.1 km in Northern Segment,whereas the slip rate averages 1.1 mm/yr and the locking depth is estimated at 1 km in the Ngalang Fault,indicating active geological movements that may influence future seismicity.
文摘The initial noise present in the depth images obtained with RGB-D sensors is a combination of hardware limitations in addition to the environmental factors,due to the limited capabilities of sensors,which also produce poor computer vision results.The common image denoising techniques tend to remove significant image details and also remove noise,provided they are based on space and frequency filtering.The updated framework presented in this paper is a novel denoising model that makes use of Boruta-driven feature selection using a Long Short-Term Memory Autoencoder(LSTMAE).The Boruta algorithm identifies the most useful depth features that are used to maximize the spatial structure integrity and reduce redundancy.An LSTMAE is then used to process these selected features and model depth pixel sequences to generate robust,noise-resistant representations.The system uses the encoder to encode the input data into a latent space that has been compressed before it is decoded to retrieve the clean image.Experiments on a benchmark data set show that the suggested technique attains a PSNR of 45 dB and an SSIM of 0.90,which is 10 dB higher than the performance of conventional convolutional autoencoders and 15 times higher than that of the wavelet-based models.Moreover,the feature selection step will decrease the input dimensionality by 40%,resulting in a 37.5%reduction in training time and a real-time inference rate of 200 FPS.Boruta-LSTMAE framework,therefore,offers a highly efficient and scalable system for depth image denoising,with a high potential to be applied to close-range 3D systems,such as robotic manipulation and gesture-based interfaces.
文摘This study aims to develop an accurate and robust machine learning model to predict the carbonation depth of fly ash concrete,overcoming the limitations of traditional predictive methods.Five ensemble-based models,such as adaptive boosting(AdaBoost),categorical boosting(CatBoost),gradient boosting regressor(GBR),hist gradient boosting regressor(HistGBR),and extreme gradient boosting(XGBoost),were developed and optimized using 729 high-quality dataset points incorporating seven input parameters,including cement,CO_(2),exposure time,water-binder ratio,fly ash,curing time,and compressive strength.Several performance evaluation metrics were used to compare the models.The GBR model emerged as the best-performing model,based on high coefficient of determination(R^(2))values and balanced error metrics across both validation and testing datasets.While all models performed exceptionally well on the training data,GBR demonstrated superior generalization capability,with R^(2) values of 0.9438 on the validation set and 0.9310 on the testing set.Furthermore,its low mean squared error(MSE),root mean square error(RMSE),mean absolute error(MAE),and median absolute error(MdAE)confirmed its robustness and accuracy.Moreover,shapley additive explanations(SHAP)analysis enhanced the interpretability of predictions,highlighting the curing time and exposure time as the most critical drivers of carbonation depth.
基金supported by the National Natural Science Foundation of China(Grant Nos.52202282,52402054,22471283,and 52202327)Natural Science Foundation of Tianjin City(Grant Nos.22JCYBJC00040,24JCQNJC00970)。
文摘High-capacity O3-type layered NiFeMn-based oxides are promising cathodes for sodium-ion batteries,though their practical deployment is constrained by the inherent limitations of Fe redox chemistry.Traditional designs generally enforcing stoichiometric symmetry(Ni=Mn)yield low Fe redox activity.Herein,we propose a valence engineering strategy that breaks conventional Ni/Mn stoichiometry to reconfigure Fe's local chemical environment and unlock unprecedented redox depth.Density functional theory(DFT)calculations reveal that the designed NaNi_(0.35)Fe_(0.225)Mn_(0.425)O_(2)cathode exhibits a reduced Bader charge on Fe(1.598 vs.1.638 in NaNi_(1/3)Fe_(1/3)Mn_(1/3)O_(2))and elevated Fe 3d orbital energy,signifying enhanced Fe redox activity.This configuration enables an exceptional Fe^(2.60+)/Fe^(3.88+)redox(1.28 e~-per Fe),delivering a reversible capacity of184.3 mAh g^(-1)within 2-4.2 V at 0.2 C,markedly exceeding the benchmark NaNi_(1/3)Fe_(1/3)Mn_(1/3)O_(2)(161.3 mAh g^(-1))with low reaction depth of Fe^(3.01+)/Fe^(3.61+).The intensified cationic redox reaction enables an ultrahigh energy density of 596 Whkg-1.The NaNi_(0.35)Fe_(0.225)Mn_(0.425)O_(2)cathode demonstrates robust performance over a broad temperature range from-15℃to 60℃.In situ and ex situ characterizations unveil a reversible O3■P3■OP2 phase transition with minimal volume change(1.88%)that circumvents detrimental deleterious O'3 intermediates and intragranular cracking.This work establishes valence engineering as a paradigm to consolidate cationic redox reaction in high-energy layered sodium oxide cathodes.
基金supported by the National Natural Science Foundation of China(Nos.52422408 and 52171031)the Liaoning Xingliao Talents-Top-notch Young Talents Project(No.XLYC2203064)+1 种基金the Excellent Youth Fund of Liaoning Natural Science Foundation(No.2023JH3/10200001)the Fundamental Research Funds for the Central Universities(No.N2425004).
文摘The transient phenomena of re-oxidation and slag entrapment occurring in the tundish during the ladle change-over process have been proven detrimental to clean steel production.Therefore,an unsteady three-phase turbulence model,coupling velocity,temperature,and phase field was established to study the effect of the ladle shroud immersion depth on the slag eye formation,slag entrainment,slag dragging,air dragging,and flow characteristics during the ladle change-over process of a two-strand tundish.The results showed that reducing the immersion depth decreases the high-velocity region area under the slag layer in the quasi-steady process.During the emptying stage,as the molten bath level gradually decreases,the outlet temperature exhibits a trend of initially decreasing and subsequently increasing across all three shroud immersion depths.However,under a 210 mm shroud immersion depth,molten slag and air are dragged into the shroud,forming slag droplets and causing significant fluctuations,with a maximum scalar velocity of 0.0764 m/s at the monitoring point.In the filling stage,air and molten slag are dragged into the molten bath,forming bubbles and slag droplets at an immersion depth of 210 mm.Bubbles are observed within the molten slag layer,which can readily cause an emulsification phenomenon,making it easier to be dragged as slag droplets.Additionally,the slag eye area measured under 210 mm immersion depth at 45 s is 0.303 m^(2),while the maximum scalar velocity of 2.4259 m/s is detected at 12 s.At an immersion depth of 360 mm,the average area of the slag eye is minimized to 0.06268 m2,with corresponding variances of 0.006753,representing the optimal immersion depth.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12274348 and 12004335)the National Key Research and Development Program of China(Grant No.2024YFC2813800)。
文摘Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters to estimate source depth accurately.Unlike traditional matched field processing(MFP)and matched mode processing(MMP),the proposed approach can estimate source depth directly from the data received by sensors without requiring complete environmental information.Firstly,the broadband Stokes parameters(BSP)are established using the normal mode theory.Then the nonstationary phase approximation is used to simplify the theoretical derivation,which is necessary when dealing with broadband integrals.Additionally,range terms of the BSP are eliminated by normalization.By analyzing the depth distribution of the normalized broadband Stokes parameters(NBSP),it is found that the NBSP exhibit extreme values at the source depth,which can be used for source depth estimation.So the proposed depth estimation method is based on searching the peaks of the NBSP.Simulations show that this method is effective in relatively simple shallow water environments.Finally,the effect of source range,frequency bandwidth,sound speed profile(SSP),water depth,and signal-to-noise ratio(SNR)are studied.The findings indicate that the proposed method can accurately estimate the source depth when the SNR is greater than-5 d B and does not need to consider model mismatch issues.Additionally,variations in environmental parameters have minimal impact on estimation accuracy.Compared to MFP,the proposed method requires a higher SNR,but demonstrates superior robustness against fluctuations in environmental parameters.
文摘Objective:To explore the relationship between anesthetic depth and surgical stress response in minimally invasive cardiothoracic surgery.Methods:A total of 89 patients who underwent thoracoscopic minimally invasive cardiothoracic surgery in our hospital from June 2024 to December 2024 were selected as the research objects.They were divided into the light anesthesia group(n=45)and the deep anesthesia group(n=44).The vital signs at different intraoperative nodes and perioperative stress status of the two groups were compared.Results:Before lesion resection and after surgery,the mean arterial pressure and heart rate of the deep anesthesia group were lower than those of the light anesthesia group,with statistically significant differences.Conclusion:In thoracoscopic minimally invasive cardiothoracic surgery,deep anesthesia can effectively control the patient’s surgical stress response,but the postoperative awakening time is longer;patients under light anesthesia have a shorter awakening time,but the intraoperative stress response is obvious.
基金supported by the National Natural Science Foundation of China(Nos.62373009 and 62073004).
文摘Compared to monocular depth estimation,multi-view depth estimation often yields more accurate results.However,traditional multi-view depth estimation methods often fail to leverage semantic information fully and struggle to effectively fuse information from multiple views,leading to suboptimal prediction performance in challenging scenarios such as texture-less regions and reflective surfaces.To address these limitations,we present MVI-Depth,a novel framework with two core innovations:(1)a Semantic Fusion Module(SFM)that establishes semantic correspondence,and(2)a Depth Updating Module(DUM)enabling iterative depth refinement.Specifically,MVI-Depth initially establishes a main view representation that integrates single-view depth,depth features,and semantic features.Subsequent feature extraction from neighbouring views enables the construction of the original cost volume.Recognising the inherent limitations of direct cost volume utilisation in complex scenes,the proposed SFM constructs an aligned semantic cost volume to utilise the complementarity between semantic and depth information,forming an improved final cost volume.The final cost volume is updated through the proposed DUM to achieve iterative depth optimisation.Comprehensive evaluations demonstrate that MVI-Depth achieves superior performance across all standard metrics on both ScanNet and KITTI benchmarks,outperforming existing methods.Additional experiments on the 7-Scenes dataset further confirm the framework's robust generalisation capabilities in diverse environments.
基金partially supported by the Natural Science Foundation of Shandong Province,China(No.ZR2023ME009)the National Natural Science Foundation of China(No.51909252)。
文摘Accurately estimating depth from underwater monocular images is essential for the target tracking task of unmanned underwater vehicles.This work proposes a method based on the Lpg-Lap Unet architecture.First,the Unet architecture integrates Laplacian pyramid depth residuals and Sobel operators to improve the boundary details in depth images,which may suffer from the feature loss caused by upsampling and the blurriness of underwater images.Multiscale local planar guidance layers then fully exploit the intermediate depth features,and a comprehensive loss function ensures robustness and accuracy.Experimental results on benchmarks demonstrate the effectiveness of Lpg-Lap Unet and its superior performance over state-of-the-art models.An underwater target tracking system is then designed to further validate its real-time capabilities in the AirSim simulation platform.
基金Supported by the National Natural Science Foundation of China(Nos.T2261149752,41976163,42476172)。
文摘Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observations in turbid coastal waters.In this paper,we developed a physicsenhanced deep neural network to estimate bathymetry of highly turbid waters of the Changjiang(Yangtze)River estuary from dual-polarized synthetic aperture radar(SAR)images.Sentinel-1A/B SAR images with a spatial resolution of 20 m×22 m were collected and matched with water depth data from nautical charts during 2017-2023.For the input parameters of the model,in addition to the normalized radar backscatter cross section(NRCS)at single polarization and incidence angle,the impacts of both polarimetric characteristics and physical environmental factors on model performance were discussed in detail.Results of feature importance analysis and sensitivity experiments indicate that the polarization ratio and NRCS after removing the influence of background sea surface wind field make significant contributions to the bathymetry retrieval model.The root mean square error(RMSE)of SAR derived water depth decreases from 1.44 to 0.78 m within 0-30-m depth,and the mean relative error(MRE)is reduced from 15.6%to 8.6%.Compared with other machine learning models such as ResNet,XGBoost,and Random Forest,the MRE is reduced by 3.9%,5.7%,and 7.4%,respectively.The spatial distribution of SAR derived water depth also exhibits a high degree of consistency with observations,demonstrating the great potential of the model in estimating the depth of turbid shallow waters.
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.
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。
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.
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 Key Research and Development Program of China(2022YFB3605902)the National Natural Science Foundation of China(52375411,52293402)。
文摘Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.
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。
