Existing sandstone rock structure evaluation methods rely on visual inspection,with low efficiency,semi-quantitative analysis of roundness,and inability to perform classified statistics in particle size analysis.This ...Existing sandstone rock structure evaluation methods rely on visual inspection,with low efficiency,semi-quantitative analysis of roundness,and inability to perform classified statistics in particle size analysis.This study presents an intelligent evaluation method for sandstone rock structure based on the Segment Anything Model(SAM).By developing a lightweight SAM fine-tuning method with rank-decomposition matrix adapters,a multispectral rock particle segmentation model named CoreSAM is constructed,which achieves rock particle edge extraction and type identification.Building upon this,we propose a comprehensive quantitative evaluation system for rock structure,assessing parameters including particle size,sorting,roundness,particle contact and cementation types.The experimental results demonstrate that CoreSAM outperforms existing methods in rock particle segmentation accuracy while showing excellent generalization across different image types such as CT scans and core photographs.The proposed method enables full-sample,classified particle size analysis and quantitative characterization of parameters like roundness,advancing reservoir evaluation towards more precise,quantitative,intuitive,and comprehensive development.展开更多
Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding ...Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.展开更多
Three model polyurethane hard segments based on dimethylol butanoic acid (DMBA) and 1,6-hexane diisocyanate (HDI), toluene diisocyanate (TDI) and 4,4'-diphenylmethane diisocyanate (MDI) were prepared by the soluti...Three model polyurethane hard segments based on dimethylol butanoic acid (DMBA) and 1,6-hexane diisocyanate (HDI), toluene diisocyanate (TDI) and 4,4'-diphenylmethane diisocyanate (MDI) were prepared by the solution method. Fourier Infrared (FTIR) spectroscopy was employed to study the H-bonds in these model polyurethanes. The model polyurethane hard segment prepared from HDI and 1,4-butanodiol (BDO) was used for comparison. It was found that the incorporation of the pendent carboxyl through DMBA into the model hard segments weakens the original NH…O = C H-bond but gives more H-bond patterns based on the two H-bond donors, urethane NH and carboxylic OH. The carboxylic dimer is one of the main H-bond types and is stronger than another main H-bond type NH…O=C. In addition, the H-bond in aromatic model hard segments is stronger than that of aliphatic hard segments. The appearance of the free C=O and the fact that almost all N—H is H-bonded suggest that there possibly exist either the third H-bond acceptor or the H-bond formed by one acceptor with two donors.展开更多
Background:The greater trochanter marker is commonly used in 3-dimensional(3D) models;however,its influence on hip and knee kinematics during gait is unclear.Understanding the influence of the greater trochanter marke...Background:The greater trochanter marker is commonly used in 3-dimensional(3D) models;however,its influence on hip and knee kinematics during gait is unclear.Understanding the influence of the greater trochanter marker is important when quantifying frontal and transverse plane hip and knee kinematics,parameters which are particularly relevant to investigate in individuals with conditions such as patellofemoral pain,knee osteoarthritis,anterior cruciate ligament(ACL) injury,and hip pain.The aim of this study was to evaluate the effect of including the greater trochanter in the construction of the thigh segment on hip and knee kinematics during gait.Methods:3D kinematics were collected in 19 healthy subjects during walking using a surface marker system.Hip and knee angles were compared across two thigh segment definitions(with and without greater trochanter) at two time points during stance:peak knee flexion(PKF) and minimum knee flexion(Min KF).Results:Hip and knee angles differed in magnitude and direction in the transverse plane at both time points.In the thigh model with the greater trochanter the hip was more externally rotated than in the thigh model without the greater trochanter(PKF:-9.34°± 5.21° vs.1.40°± 5.22°,Min KF:-5.68°± 4.24° vs.5.01°± 4.86°;p < 0.001).In the thigh model with the greater trochanter,the knee angle was more internally rotated compared to the knee angle calculated using the thigh definition without the greater trochanter(PKF:14.67°± 6.78° vs.4.33°± 4.18°,Min KF:10.54°± 6.71° vs.-0.01°± 2.69°;p < 0.001).Small but significant differences were detected in the sagittal and frontal plane angles at both time points(p < 0.001).Conclusion:Hip and knee kinematics differed across different segment definitions including or excluding the greater trochanter marker,especially in the transverse plane.Therefore when considering whether to include the greater trochanter in the thigh segment model when using a surface markers to calculate 3D kinematics for movement assessment,it is important to have a clear understanding of the effect of different marker sets and segment models in use.展开更多
The use of AI technologies in remote sensing(RS)tasks has been the focus of many individuals in both the professional and academic domains.Having more accessible interfaces and tools that allow people of little or no ...The use of AI technologies in remote sensing(RS)tasks has been the focus of many individuals in both the professional and academic domains.Having more accessible interfaces and tools that allow people of little or no experience to intuitively interact with RS data of multiple formats is a potential provided by this integration.However,the use of AI and AI agents to help automate RS-related tasks is still in its infancy stage,with some frameworks and interfaces built on top of well-known vision language models(VLM)such as GPT-4,segment anything model(SAM),and grounding DINO.These tools do promise and draw guidelines on the potentials and limitations of existing solutions concerning the use of said models.In this work,the state of the art AI foundation models(FM)are reviewed and used in a multi-modal manner to ingest RS imagery input and perform zero-shot object detection using natural language.The natural language input is then used to define the classes or labels the model should look for,then,both inputs are fed to the pipeline.The pipeline presented in this work makes up for the shortcomings of the general knowledge FMs by stacking pre-processing and post-processing applications on top of the FMs;these applications include tiling to produce uniform patches of the original image for faster detection,outlier rejection of redundant bounding boxes using statistical and machine learning methods.The pipeline was tested with UAV,aerial and satellite images taken over multiple areas.The accuracy for the semantic segmentation showed improvement from the original 64%to approximately 80%-99%by utilizing the pipeline and techniques proposed in this work.GitHub Repository:MohanadDiab/LangRS.展开更多
Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in ord...Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative models.However,these methods required a mass of computation of training or searching.In this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for images.Without the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved annotations.In this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation model.Multiple comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and COCO2017.On this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,respectively.Consequently,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.展开更多
CO_(2) storage capacity is significantly influenced by the saturation levels of reservoir rocks,with underground fluid saturation typically evaluated using resistivity data.The conductive pathways of fluids in various...CO_(2) storage capacity is significantly influenced by the saturation levels of reservoir rocks,with underground fluid saturation typically evaluated using resistivity data.The conductive pathways of fluids in various states within rock pores differ,alongside variations in conductive mechanisms.To clarify the conductivity of water in rocks across different states,this study employed a three-pore segment saturation model,which corrected for the additional conductivity of clay by categorizing water into large-pore segment,medium-pore segment,and small-pore segment types.Addressing the heterogeneity of tight sandstone reservoirs,we classified distinct pore structures and inverted Archie equation parameters from NMR logging data using a segmented characterization approach,yielding dynamic Archie parameters that vary with depth.Ultimately,we established an improved saturation parameter method based on joint inversion of NMR and resistivity data,which was validated through laboratory experiments and practical downhole applications.The results indicate that this saturation parameter inversion method has been effectively applied in both settings.Furthermore,we discussed the varying conductive behaviors of fluids in large and medium pore segment under saturated and drained states.Lastly,we proposed a workflow for inverting saturation based on downhole data,providing a robust foundation for CO_(2) storage and predicting underground fluid saturation.展开更多
Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex str...Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex structural optimization problems, if the structural reanalysis technique is not adopted, the more the number of finite element analysis (FEA) is, the more the consuming time is. In the conventional structural optimization the number of FEA can be reduced by the structural reanalysis technique based on the approximation techniques and sensitivity analysis. With these techniques, this paper provides a new approximation model-segment approximation model, adopted for the GA application. This segment approximation model can decrease the number of FEA and increase the convergence rate of GA. So it can apparently decrease the computation time of GA. Two examples demonstrate the availability of the new segment approximation model.展开更多
In this study, compatible taper and stem volume equations were developed for Larix kaempferi species of South Korea. The dataset was split into two groups： 80% of the data were used in model fitting and the remaining...In this study, compatible taper and stem volume equations were developed for Larix kaempferi species of South Korea. The dataset was split into two groups： 80% of the data were used in model fitting and the remaining 2o% were used for validation. The compatible MB76 equations were used to predict the diameter outside bark to a specific height, the height to a specific diameter and the stem volume of the species. The result of the stem volume analysis was compared with the existing stem volume model of Larix kaempferi species of South Korea which was developed by the Korea Forest Research Institute and with a simple volume model that was developed with fitting dataset in this study. The compatible model provided accurate prediction of the total stem volume when compared to the existing stem volume model and with a simple volume model. It is concluded that the compatible taper and stem volume equations are more convenient to use and therefore it is recommended to be applied in the Larix kaempferi species of South Korea.展开更多
Estimating individual tree volume is one of the essential building blocks in forest growth and yield models. Ecologically based taper equations provide accurate vol- ume predictions and allow classification by mer- ch...Estimating individual tree volume is one of the essential building blocks in forest growth and yield models. Ecologically based taper equations provide accurate vol- ume predictions and allow classification by mer- chantable sizes, assisting in sustainable forest management. In the present study, ecoregion-based compatible volume systems for brutian pine and black pine in the three ecoregions of southern Turkey were developed. Several well-known taper functions were evaluated. A second- order continuous-time autoregressive error structure was used to correct the inherent autocorrelation in the hierar- chical data, allowing the model to be applied to irregularly spaced and unbalanced data. The compatible segmented model of Fang et al. （For Sci 46：1-12, 2000） best described the experimental data. It is therefore recommended for estimating diameter at a specific height, height to a specific diameter, merchantable volume, and total volume for the three ecoregions and two species analyzed. The nonlinearextra sum of squares method indicated differences in ecoregion and tree-specific taper functions. A different taper function should therefore be used for each pine spe- cies and ecoregion in southern Turkey. Using ecoregion- specific taper equations allows making more robust esti- mations and, therefore, will enhance the accuracy of diameter at different heights and volume predictions.展开更多
The precooler is a distinctive component of precooled air-breathing engines but constitutes a challenge to conventional thermal design methods.The latter are based upon assumptions that often reveal to be limited for ...The precooler is a distinctive component of precooled air-breathing engines but constitutes a challenge to conventional thermal design methods.The latter are based upon assumptions that often reveal to be limited for precooler design.In this paper,a refined design method considering the variations of fluid thermophysical properties,flow area and thermal parameters distortion,was proposed to remediate their limitations.Firstly,the precooler was discretized into a fixed number of sub-microtubes based on a new discretization criterion.Next,in-house one-dimensional(1D)and two-dimensional(2D)segmented models were established for rapid thermal design and precooler rating with non-uniform airflow,respectively.The heat transfer experimental studies of supercritical hydrocarbon fuel were performed to verify the Jackson correlation for precooler design and the in-house models were validated against the reported data from open literature.On this basis,the proposed method was employed for the design analysis of hydrocarbon fuel precoolers for precooled-Turbine Based Combined Cycle(TBCC)engines.The results show that the local performance of precoolers is intrinsically impacted by the aforementioned three variations.In the case study,the local heat transfer performance is drastically affected by coolant flow transition.While the circumferential temperature distortion of airflow is weakened by heat transfer.With consideration of additional parameter variations,this novel method improves design accuracy and shortens the design time.展开更多
“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素...“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素在色彩信息和外形特征上差异较小,如何从二维影像中智能精准地识别“精灵圈”像素并对识别的单个像素形成个体“精灵圈”是目前的技术难点。本文提出了一种结合分割万物模型(Segment Anything Model,SAM)视觉分割模型与随机森林机器学习的无人机影像“精灵圈”分割及分类方法,实现了单个“精灵圈”的识别和提取。首先,通过构建索伦森-骰子系数(S?rensen-Dice coefficient,Dice)和交并比(Intersection over Union,IOU)评价指标,从SAM中筛选预训练模型并对其参数进行优化,实现全自动影像分割,得到无属性信息的分割掩码/分割类;然后,利用红、绿、蓝(RGB)三通道信息及空间二维坐标将分割掩码与原图像进行信息匹配,构造分割掩码的特征指标,并根据袋外数据(Out of Bag,OOB)误差减小及特征分布规律对特征进行分析和筛选;最后,利用筛选的特征对随机森林模型进行训练,实现“精灵圈”植被、普通植被和光滩的自动识别与分类。实验结果表明:本文方法“精灵圈”平均正确提取率96.1%,平均错误提取率为9.5%,为精准刻画“精灵圈”时空格局及海岸带无人机遥感图像处理提供了方法和技术支撑。展开更多
We developed a simple polynomial taper equation for poplars growing on former farmland in Sweden and also evaluated the performance of some well-known taper equations. In Sweden there is an increasing interest in the ...We developed a simple polynomial taper equation for poplars growing on former farmland in Sweden and also evaluated the performance of some well-known taper equations. In Sweden there is an increasing interest in the use of poplar. Effective management of poplar plantations for high yield production would be facilitated by taper equations providing better predictions of stem volume than currently available equations. In the study a polynomial stem taper equation with five parameters was established for individual poplar trees growing on former farmland. The outputs of the polynomial taper equation were compared with five published equations. Data for fitting the equations were collected from 69 poplar trees growing at 37 stands in central and southern Sweden （lat. 55–60° N）. The mean age of the stands was 21 years （range 14–43）, the mean density 984 stems·ha？1 （198–3,493）, and the mean diameter at breast height （outside bark） 25 cm （range 12–40）. To verify the tested equations, performance of accuracy and precision diameter predictions at seven points along the stem was closely analyzed. Statistics used for evaluation of the equations indicated that the variable exponent taper equation presented by Kozak （1988） performed best and can be recommended. The stem taper equation by Kozak （1988） recommended in the study is likely to be beneficial for optimising the efficiency and profitability of poplar plantation management. The constructed polynomial equation and the segmented equation presented by Max ＆ Burkhart （1976） were second and third ranked. Due to the statistical complexity of Kozak’s equation, the constructed polynomial equation is alternatively recommended when a simple model is requested and larger bias is accepted.展开更多
Taper equations are an important tool in estimating stem volumes at a multi-product level for sustainable forest management.Nine taper equations are tested from three categories to estimate diameter at specified point...Taper equations are an important tool in estimating stem volumes at a multi-product level for sustainable forest management.Nine taper equations are tested from three categories to estimate diameter at specified point on the stem,height at specified diameter,volumes of any desired portion of the stem,and whole tree volume of Taurus fir in Taurus Mountains of Turkey.To account for autocorrelation and multicollinearity present among multiple stem data observations collected from the same tree.proper statistical approaches were used in model fitting.Comparisons are made to determine which equation provides the best overall fit to all data based on four goodness-of-fit statistics,Coefficient of determination(R2);Root mean square error(RMSE),Akaike’s Information Criterion(AIC),and Bayesian information criterion(BIC).Results indicated that all taper equations tested could be used to accurately estimate section diameter at given height and stem volume.Clark et al.’s taper equation provided better results than the others for Taurus fir when an additional stem diameter observation at 5.30 m was available.Segmented and variable-form taper models consistently provided better results than the simple taper models except for Max and Burkhart’s model.Fang et al.’s and Kozak’s taper models showed equally good performance to describe stem taper and to predict tree stem volume.Therefore,these taper equations are able to be used to estimate diameter and volume for Taurus fir trees,if an upper stem diameter measurement was not available.展开更多
The desire to benefit from economy of scale is one of the major driving forces behind the continuous growth in ship sizes. However, models of new large ships need to be thoroughly investigated to determine the carrier...The desire to benefit from economy of scale is one of the major driving forces behind the continuous growth in ship sizes. However, models of new large ships need to be thoroughly investigated to determine the carrier's response in waves. In this work, experimental and numerical assessments of the motion and load response of a 550,000 DWT ore carrier are performed using prototype ships with softer stiffness, and towing tank tests are conducted using a segmented model with two schemes of softer stiffness. Numerical analyses are performed employing both rigid body and linear hydroelasticity theories using an in-house program and a comparison is then made between experimental and numerical results to establish the influence of stiffness on the ore carrier's springing response. Results show that softer stiffness models can be used when studying the springing response of ships in waves.展开更多
基金Supported by the National Natural Science Foundation of China(42372175,72088101)PetroChina Science and Technology Project of(2023DJ84)Basic Research Cooperation Project between China National Petroleum Corporation and Peking University.
文摘Existing sandstone rock structure evaluation methods rely on visual inspection,with low efficiency,semi-quantitative analysis of roundness,and inability to perform classified statistics in particle size analysis.This study presents an intelligent evaluation method for sandstone rock structure based on the Segment Anything Model(SAM).By developing a lightweight SAM fine-tuning method with rank-decomposition matrix adapters,a multispectral rock particle segmentation model named CoreSAM is constructed,which achieves rock particle edge extraction and type identification.Building upon this,we propose a comprehensive quantitative evaluation system for rock structure,assessing parameters including particle size,sorting,roundness,particle contact and cementation types.The experimental results demonstrate that CoreSAM outperforms existing methods in rock particle segmentation accuracy while showing excellent generalization across different image types such as CT scans and core photographs.The proposed method enables full-sample,classified particle size analysis and quantitative characterization of parameters like roundness,advancing reservoir evaluation towards more precise,quantitative,intuitive,and comprehensive development.
基金supported by Natural Science Foundation Programme of Gansu Province(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Science and Technology Plan Key Research and Development Program Project(No.24YFFA024).
文摘Despite its remarkable performance on natural images,the segment anything model(SAM)lacks domain-specific information in medical imaging.and faces the challenge of losing local multi-scale information in the encoding phase.This paper presents a medical image segmentation model based on SAM with a local multi-scale feature encoder(LMSFE-SAM)to address the issues above.Firstly,based on the SAM,a local multi-scale feature encoder is introduced to improve the representation of features within local receptive field,thereby supplying the Vision Transformer(ViT)branch in SAM with enriched local multi-scale contextual information.At the same time,a multiaxial Hadamard product module(MHPM)is incorporated into the local multi-scale feature encoder in a lightweight manner to reduce the quadratic complexity and noise interference.Subsequently,a cross-branch balancing adapter is designed to balance the local and global information between the local multi-scale feature encoder and the ViT encoder in SAM.Finally,to obtain smaller input image size and to mitigate overlapping in patch embeddings,the size of the input image is reduced from 1024×1024 pixels to 256×256 pixels,and a multidimensional information adaptation component is developed,which includes feature adapters,position adapters,and channel-spatial adapters.This component effectively integrates the information from small-sized medical images into SAM,enhancing its suitability for clinical deployment.The proposed model demonstrates an average enhancement ranging from 0.0387 to 0.3191 across six objective evaluation metrics on BUSI,DDTI,and TN3K datasets compared to eight other representative image segmentation models.This significantly enhances the performance of the SAM on medical images,providing clinicians with a powerful tool in clinical diagnosis.
基金This work was supported by the Natural Science Foundation of Henan Province (004030600)
文摘Three model polyurethane hard segments based on dimethylol butanoic acid (DMBA) and 1,6-hexane diisocyanate (HDI), toluene diisocyanate (TDI) and 4,4'-diphenylmethane diisocyanate (MDI) were prepared by the solution method. Fourier Infrared (FTIR) spectroscopy was employed to study the H-bonds in these model polyurethanes. The model polyurethane hard segment prepared from HDI and 1,4-butanodiol (BDO) was used for comparison. It was found that the incorporation of the pendent carboxyl through DMBA into the model hard segments weakens the original NH…O = C H-bond but gives more H-bond patterns based on the two H-bond donors, urethane NH and carboxylic OH. The carboxylic dimer is one of the main H-bond types and is stronger than another main H-bond type NH…O=C. In addition, the H-bond in aromatic model hard segments is stronger than that of aliphatic hard segments. The appearance of the free C=O and the fact that almost all N—H is H-bonded suggest that there possibly exist either the third H-bond acceptor or the H-bond formed by one acceptor with two donors.
基金the National Institute of Child Health and Human Development (No.NICHD,No.R15HD059080,and No.R15HD059080-01A1S1)
文摘Background:The greater trochanter marker is commonly used in 3-dimensional(3D) models;however,its influence on hip and knee kinematics during gait is unclear.Understanding the influence of the greater trochanter marker is important when quantifying frontal and transverse plane hip and knee kinematics,parameters which are particularly relevant to investigate in individuals with conditions such as patellofemoral pain,knee osteoarthritis,anterior cruciate ligament(ACL) injury,and hip pain.The aim of this study was to evaluate the effect of including the greater trochanter in the construction of the thigh segment on hip and knee kinematics during gait.Methods:3D kinematics were collected in 19 healthy subjects during walking using a surface marker system.Hip and knee angles were compared across two thigh segment definitions(with and without greater trochanter) at two time points during stance:peak knee flexion(PKF) and minimum knee flexion(Min KF).Results:Hip and knee angles differed in magnitude and direction in the transverse plane at both time points.In the thigh model with the greater trochanter the hip was more externally rotated than in the thigh model without the greater trochanter(PKF:-9.34°± 5.21° vs.1.40°± 5.22°,Min KF:-5.68°± 4.24° vs.5.01°± 4.86°;p < 0.001).In the thigh model with the greater trochanter,the knee angle was more internally rotated compared to the knee angle calculated using the thigh definition without the greater trochanter(PKF:14.67°± 6.78° vs.4.33°± 4.18°,Min KF:10.54°± 6.71° vs.-0.01°± 2.69°;p < 0.001).Small but significant differences were detected in the sagittal and frontal plane angles at both time points(p < 0.001).Conclusion:Hip and knee kinematics differed across different segment definitions including or excluding the greater trochanter marker,especially in the transverse plane.Therefore when considering whether to include the greater trochanter in the thigh segment model when using a surface markers to calculate 3D kinematics for movement assessment,it is important to have a clear understanding of the effect of different marker sets and segment models in use.
文摘The use of AI technologies in remote sensing(RS)tasks has been the focus of many individuals in both the professional and academic domains.Having more accessible interfaces and tools that allow people of little or no experience to intuitively interact with RS data of multiple formats is a potential provided by this integration.However,the use of AI and AI agents to help automate RS-related tasks is still in its infancy stage,with some frameworks and interfaces built on top of well-known vision language models(VLM)such as GPT-4,segment anything model(SAM),and grounding DINO.These tools do promise and draw guidelines on the potentials and limitations of existing solutions concerning the use of said models.In this work,the state of the art AI foundation models(FM)are reviewed and used in a multi-modal manner to ingest RS imagery input and perform zero-shot object detection using natural language.The natural language input is then used to define the classes or labels the model should look for,then,both inputs are fed to the pipeline.The pipeline presented in this work makes up for the shortcomings of the general knowledge FMs by stacking pre-processing and post-processing applications on top of the FMs;these applications include tiling to produce uniform patches of the original image for faster detection,outlier rejection of redundant bounding boxes using statistical and machine learning methods.The pipeline was tested with UAV,aerial and satellite images taken over multiple areas.The accuracy for the semantic segmentation showed improvement from the original 64%to approximately 80%-99%by utilizing the pipeline and techniques proposed in this work.GitHub Repository:MohanadDiab/LangRS.
基金Natural Science Foundation of Zhejiang Province,Grant/Award Number:LY23F020025Science and Technology Commissioner Program of Huzhou,Grant/Award Number:2023GZ42Sichuan Provincial Science and Technology Support Program,Grant/Award Numbers:2023ZHCG0005,2023ZHCG0008。
文摘Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset.Initially,data augmentation mainly involved some simple transformations of images.Later,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative models.However,these methods required a mass of computation of training or searching.In this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for images.Without the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved annotations.In this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation model.Multiple comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and COCO2017.On this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,respectively.Consequently,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.
基金supported in part by the Key National Research Project of China under Grant 2023YFC3707900in part by the National Natural Science Foundation of China under Grant 42204122 and Grant 42072323。
文摘CO_(2) storage capacity is significantly influenced by the saturation levels of reservoir rocks,with underground fluid saturation typically evaluated using resistivity data.The conductive pathways of fluids in various states within rock pores differ,alongside variations in conductive mechanisms.To clarify the conductivity of water in rocks across different states,this study employed a three-pore segment saturation model,which corrected for the additional conductivity of clay by categorizing water into large-pore segment,medium-pore segment,and small-pore segment types.Addressing the heterogeneity of tight sandstone reservoirs,we classified distinct pore structures and inverted Archie equation parameters from NMR logging data using a segmented characterization approach,yielding dynamic Archie parameters that vary with depth.Ultimately,we established an improved saturation parameter method based on joint inversion of NMR and resistivity data,which was validated through laboratory experiments and practical downhole applications.The results indicate that this saturation parameter inversion method has been effectively applied in both settings.Furthermore,we discussed the varying conductive behaviors of fluids in large and medium pore segment under saturated and drained states.Lastly,we proposed a workflow for inverting saturation based on downhole data,providing a robust foundation for CO_(2) storage and predicting underground fluid saturation.
文摘Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex structural optimization problems, if the structural reanalysis technique is not adopted, the more the number of finite element analysis (FEA) is, the more the consuming time is. In the conventional structural optimization the number of FEA can be reduced by the structural reanalysis technique based on the approximation techniques and sensitivity analysis. With these techniques, this paper provides a new approximation model-segment approximation model, adopted for the GA application. This segment approximation model can decrease the number of FEA and increase the convergence rate of GA. So it can apparently decrease the computation time of GA. Two examples demonstrate the availability of the new segment approximation model.
基金the Korea Forest Service for funding this research(Project No.S211316L020130)
文摘In this study, compatible taper and stem volume equations were developed for Larix kaempferi species of South Korea. The dataset was split into two groups： 80% of the data were used in model fitting and the remaining 2o% were used for validation. The compatible MB76 equations were used to predict the diameter outside bark to a specific height, the height to a specific diameter and the stem volume of the species. The result of the stem volume analysis was compared with the existing stem volume model of Larix kaempferi species of South Korea which was developed by the Korea Forest Research Institute and with a simple volume model that was developed with fitting dataset in this study. The compatible model provided accurate prediction of the total stem volume when compared to the existing stem volume model and with a simple volume model. It is concluded that the compatible taper and stem volume equations are more convenient to use and therefore it is recommended to be applied in the Larix kaempferi species of South Korea.
基金financially supported by the Scientific and Technological Research Council of Turkey(Project No:109 O 714)
文摘Estimating individual tree volume is one of the essential building blocks in forest growth and yield models. Ecologically based taper equations provide accurate vol- ume predictions and allow classification by mer- chantable sizes, assisting in sustainable forest management. In the present study, ecoregion-based compatible volume systems for brutian pine and black pine in the three ecoregions of southern Turkey were developed. Several well-known taper functions were evaluated. A second- order continuous-time autoregressive error structure was used to correct the inherent autocorrelation in the hierar- chical data, allowing the model to be applied to irregularly spaced and unbalanced data. The compatible segmented model of Fang et al. （For Sci 46：1-12, 2000） best described the experimental data. It is therefore recommended for estimating diameter at a specific height, height to a specific diameter, merchantable volume, and total volume for the three ecoregions and two species analyzed. The nonlinearextra sum of squares method indicated differences in ecoregion and tree-specific taper functions. A different taper function should therefore be used for each pine spe- cies and ecoregion in southern Turkey. Using ecoregion- specific taper equations allows making more robust esti- mations and, therefore, will enhance the accuracy of diameter at different heights and volume predictions.
基金co-supported by the Specialized Research Foundation of Civil Aircraft,China(MJ-2016-D-35)the Advanced Jet Propulsion Creativity Center,AEAC,China(HKCX2019-01-004)。
文摘The precooler is a distinctive component of precooled air-breathing engines but constitutes a challenge to conventional thermal design methods.The latter are based upon assumptions that often reveal to be limited for precooler design.In this paper,a refined design method considering the variations of fluid thermophysical properties,flow area and thermal parameters distortion,was proposed to remediate their limitations.Firstly,the precooler was discretized into a fixed number of sub-microtubes based on a new discretization criterion.Next,in-house one-dimensional(1D)and two-dimensional(2D)segmented models were established for rapid thermal design and precooler rating with non-uniform airflow,respectively.The heat transfer experimental studies of supercritical hydrocarbon fuel were performed to verify the Jackson correlation for precooler design and the in-house models were validated against the reported data from open literature.On this basis,the proposed method was employed for the design analysis of hydrocarbon fuel precoolers for precooled-Turbine Based Combined Cycle(TBCC)engines.The results show that the local performance of precoolers is intrinsically impacted by the aforementioned three variations.In the case study,the local heat transfer performance is drastically affected by coolant flow transition.While the circumferential temperature distortion of airflow is weakened by heat transfer.With consideration of additional parameter variations,this novel method improves design accuracy and shortens the design time.
文摘“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素在色彩信息和外形特征上差异较小,如何从二维影像中智能精准地识别“精灵圈”像素并对识别的单个像素形成个体“精灵圈”是目前的技术难点。本文提出了一种结合分割万物模型(Segment Anything Model,SAM)视觉分割模型与随机森林机器学习的无人机影像“精灵圈”分割及分类方法,实现了单个“精灵圈”的识别和提取。首先,通过构建索伦森-骰子系数(S?rensen-Dice coefficient,Dice)和交并比(Intersection over Union,IOU)评价指标,从SAM中筛选预训练模型并对其参数进行优化,实现全自动影像分割,得到无属性信息的分割掩码/分割类;然后,利用红、绿、蓝(RGB)三通道信息及空间二维坐标将分割掩码与原图像进行信息匹配,构造分割掩码的特征指标,并根据袋外数据(Out of Bag,OOB)误差减小及特征分布规律对特征进行分析和筛选;最后,利用筛选的特征对随机森林模型进行训练,实现“精灵圈”植被、普通植被和光滩的自动识别与分类。实验结果表明:本文方法“精灵圈”平均正确提取率96.1%,平均错误提取率为9.5%,为精准刻画“精灵圈”时空格局及海岸带无人机遥感图像处理提供了方法和技术支撑。
基金financially supported by Skogssll-skapet foundation
文摘We developed a simple polynomial taper equation for poplars growing on former farmland in Sweden and also evaluated the performance of some well-known taper equations. In Sweden there is an increasing interest in the use of poplar. Effective management of poplar plantations for high yield production would be facilitated by taper equations providing better predictions of stem volume than currently available equations. In the study a polynomial stem taper equation with five parameters was established for individual poplar trees growing on former farmland. The outputs of the polynomial taper equation were compared with five published equations. Data for fitting the equations were collected from 69 poplar trees growing at 37 stands in central and southern Sweden （lat. 55–60° N）. The mean age of the stands was 21 years （range 14–43）, the mean density 984 stems·ha？1 （198–3,493）, and the mean diameter at breast height （outside bark） 25 cm （range 12–40）. To verify the tested equations, performance of accuracy and precision diameter predictions at seven points along the stem was closely analyzed. Statistics used for evaluation of the equations indicated that the variable exponent taper equation presented by Kozak （1988） performed best and can be recommended. The stem taper equation by Kozak （1988） recommended in the study is likely to be beneficial for optimising the efficiency and profitability of poplar plantation management. The constructed polynomial equation and the segmented equation presented by Max ＆ Burkhart （1976） were second and third ranked. Due to the statistical complexity of Kozak’s equation, the constructed polynomial equation is alternatively recommended when a simple model is requested and larger bias is accepted.
基金the part of Ph D dissertation and funded by the Suleyman Demirel University-Teaching Staff Training Program (Project number: OYP05250-DR-14)。
文摘Taper equations are an important tool in estimating stem volumes at a multi-product level for sustainable forest management.Nine taper equations are tested from three categories to estimate diameter at specified point on the stem,height at specified diameter,volumes of any desired portion of the stem,and whole tree volume of Taurus fir in Taurus Mountains of Turkey.To account for autocorrelation and multicollinearity present among multiple stem data observations collected from the same tree.proper statistical approaches were used in model fitting.Comparisons are made to determine which equation provides the best overall fit to all data based on four goodness-of-fit statistics,Coefficient of determination(R2);Root mean square error(RMSE),Akaike’s Information Criterion(AIC),and Bayesian information criterion(BIC).Results indicated that all taper equations tested could be used to accurately estimate section diameter at given height and stem volume.Clark et al.’s taper equation provided better results than the others for Taurus fir when an additional stem diameter observation at 5.30 m was available.Segmented and variable-form taper models consistently provided better results than the simple taper models except for Max and Burkhart’s model.Fang et al.’s and Kozak’s taper models showed equally good performance to describe stem taper and to predict tree stem volume.Therefore,these taper equations are able to be used to estimate diameter and volume for Taurus fir trees,if an upper stem diameter measurement was not available.
基金Supported by the National Natural Science Foundation of China （Grant No. 51079034）, and the National Basic Research Program of China （Grant No. 2011CB013703）
文摘The desire to benefit from economy of scale is one of the major driving forces behind the continuous growth in ship sizes. However, models of new large ships need to be thoroughly investigated to determine the carrier's response in waves. In this work, experimental and numerical assessments of the motion and load response of a 550,000 DWT ore carrier are performed using prototype ships with softer stiffness, and towing tank tests are conducted using a segmented model with two schemes of softer stiffness. Numerical analyses are performed employing both rigid body and linear hydroelasticity theories using an in-house program and a comparison is then made between experimental and numerical results to establish the influence of stiffness on the ore carrier's springing response. Results show that softer stiffness models can be used when studying the springing response of ships in waves.
