近年来河流生态系统面临着水资源、流量调节不均的情况,生态流量是河流维持生态功能的重要指标。因此生态流量分析对于维持河流生态系统平衡与稳定、保护生物多样性以及提供各种生态系统服务具有重要意义。研究基于SWAT(Soil and Water ...近年来河流生态系统面临着水资源、流量调节不均的情况,生态流量是河流维持生态功能的重要指标。因此生态流量分析对于维持河流生态系统平衡与稳定、保护生物多样性以及提供各种生态系统服务具有重要意义。研究基于SWAT(Soil and Water Assessment Tool)模型,对海浪河流域进行了生态流量分析。构建天然径流与实际径流模型,分析土地利用变化对径流影响,结合Tennant法计算生态流量阈值,分析流域水量时空分布特征。对比不同水文年型下径流差异,并量化生态流量盈余空间格局。结果表明,天然径流与实际径流在年均分布和总量上存在差异,尤其是在汛期(5-9月),实际径流明显低于天然径流,而非汛期(10月至4月)变化较小;实际径流受到人类活动因素的影响,导致2、12、19、24号子流域水资源量略低于天然径流;由土地利用变化分析发现海浪河流域2005年土地利用模拟径流相较2000年径流下降4.25%,2010年土地利用模拟径流相较2005年径流下降1.56%,2015年土地利用模拟径流相较2010年径流上升0.99%。海浪河流域的生态流量需求均符合Tennant法中“最佳”标准等级的要求。生态流量盈余分析表明,流域水量在空间上呈现不均衡分布。研究提出需优化水资源调度策略以应对季节性供需失衡,为寒区河流生态流量管理提供模型融合分析范式。展开更多
Glacial meltwater constitutes a vital component of the water supply in arid and semi-arid areas.However,the influence of glacial melting on runoff and evapotranspiration under global warming remains insufficiently und...Glacial meltwater constitutes a vital component of the water supply in arid and semi-arid areas.However,the influence of glacial melting on runoff and evapotranspiration under global warming remains insufficiently understood.Previous studies coupling the Soil and Water Assessment Tool(SWAT)model with glacier modules often failed to consider the spatial heterogeneity of temperature during glacial melting,potentially leading to biased estimates of meltwater volume.In this study,we developed a glacier-coupled SWAT(SWAT-glacier)model considering the digital elevation model(DEM)based temperature-driven glacial melt processes to elucidate the impact of glacial melting on hydrological processes across four river basins(Dongda,Xiying,Jinta,and Zamu)of the upper Shiyang River Basin(SYRB)in northwestern China from 1986 to 2021.Compared with the standard SWAT model,the proposed SWAT-glacier model significantly improved the simulation accuracy for both runoff and evapotranspiration.Specifically,in comparison with the standard SWAT model,the Nash-Sutcliffe efficiency of the SWAT-glacier model showed a relative improvement of approximately 0.42%–9.16%and 1.50%–10.15%for runoff and evapotranspiration,respectively,in the four river basins during the validation period.Annual glacial runoff occurred predominantly from May to October,whereas glacial melt-induced evapotranspiration peaked between June and August.From 1986 to 2021,the average contributions of glacial melt to runoff were 6.97%for Dongda,3.06%for Xiying,2.70%for Jinta,and 0.67%for Zamu,whereas its contributions to evapotranspiration were 9.06%,5.14%,3.21%,and 1.59%,respectively.This study presents a SWAT-glacier modeling framework that enhances the simulation of hydrological processes in cold regions.The proposed methodology can be extended to other glacierized basins to provide valuable insights into water resource management under climate change.展开更多
水文模拟精度决定着流域水资源丰枯状态,开展水文模型不确定性研究可有效降低模拟结果的不确定性,从而提高径流组成成分模拟精度。为此,本文以汉江上游的子午河流域为例,提出一种改进的第二代非支配排序算法(Non-dominated Sorting Gene...水文模拟精度决定着流域水资源丰枯状态,开展水文模型不确定性研究可有效降低模拟结果的不确定性,从而提高径流组成成分模拟精度。为此,本文以汉江上游的子午河流域为例,提出一种改进的第二代非支配排序算法(Non-dominated Sorting Genetic Algorithm-Ⅱ,NSGA-Ⅱ)多目标算法校准分布式SWAT(Soil and Water Assessment Tool)模型,并将其与基于SWAT-CUP软件下SUFI-2算法的模拟结果进行对比分析。同时,采用Mann-Kendall趋势检验方法研究分析各子流域径流组成的趋势性演变特征。结果表明,改进的NSGA-Ⅱ算法能够较好地模拟子午河流域的水文过程,且模拟精度优于SUFI-2算法;其模拟的流域实际蒸发量、土壤蓄水量、总产水量和地下径流量精度均优于后者,而后者低估了各子流域的土壤蓄水量,尤其是地下径流量。研究结果对于流域水资源量的精准预估具有重要的指导意义。展开更多
Soil erosion is a fundamental physical process driving land degradation across various spatial and temporal scales.The Soil and Water Assessment Tool(SWAT)model is a robust tool for predicting soil erosion and evaluat...Soil erosion is a fundamental physical process driving land degradation across various spatial and temporal scales.The Soil and Water Assessment Tool(SWAT)model is a robust tool for predicting soil erosion and evaluating water and soil quality within watersheds.The latest version,SWAT+,introduces advanced encoding capabilities and improved performance,making it better suited for addressing complex watershed modeling challenges.This study implemented the SWAT+model to quantify soil erosion rates within the Chehelchay watershed in northern Iran.The foundational dataset comprises a 30-meter resolution Digital Elevation Model(DEM),land use classification,soil,and weather data.Model performance was evaluated using Nash-Sutcliffe Efficiency(NSE),coefficient of determination(R^(2)),root mean square error(RMSE),and percent bias(PBIAS).The SWAT+simulation revealed substantial spatial variation in erosion patterns across the watershed,with annual sediment yields in critical HRUs,reflecting diverse erosion intensities driven by variations in land use,soil characteristics,and slope.Among the Hydrological Response Units(HRUs),50 critical units,representing approximately 9%of the total watershed area,generate sediment yields exceeding 5 tons per hectare per year.The most severe erosion occurs predominantly in the central zone of the watershed.Downstream regions exhibit minimal soil loss due to gentle topography while upstream areas maintain soil stability through protective forest cover,resulting in negligible erosion rates.Best Management Practices(BMPs)were designed to safeguard water and soil resources at a watershed level.The study evaluated three strategic conservation interventions:alfalfa cultivation,agroforestry implementation,and garden development.When applied in combination,these measures achieved approximately 30%reduction in sediment yield at the HRU level.This integrated approach demonstrates the potential of combining multiple land management strategies to combat erosion effectively.展开更多
Off-axis aspherical mirrors are widely used in optical systems and precision measuring instruments,whereas off-axis aspherical mirrors with large sizes and off-axis are used in large optical systems such as astronomic...Off-axis aspherical mirrors are widely used in optical systems and precision measuring instruments,whereas off-axis aspherical mirrors with large sizes and off-axis are used in large optical systems such as astronomical telescopes and radio telescopes.However,if the off-axis amount of an off-axis aspherical mirror exceeds the capability of the machine tool,traditional rotary-turning machining methods are not applicable,and advanced computerized numerical control(CNC)machining methods,such as the slow-tool-servo method,must be im-plemented.This article proposes a non-conventional offset(NCO)fabrication method based on slow-tool-servo single-point diamond turning for machining off-axis aspherical surfaces with large off-axis amounts.This method is theoretically applicable to the machining of off-axis aspherical surfaces with any off-axis amount.NCO fab-rication is a simpler and more efficient path-planning solution for machining individual off-axis parabolic sur-faces.In addition,corresponding solutions for other types of aspherical surfaces are proposed using the NCO method.The turning depths of workpieces with different off-axis amounts at the same machining position are analyzed and compared.A specific measurement scheme for the NCO method is presented,and the experimental results indicate that the PV and RMS form errors are 0.658μm and 60 nm,respectively.This work demonstrates that the NCO method can effectively deal with the machining challenges of off-axis aspherical structures with large off-axis amounts.展开更多
Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods...Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.展开更多
The surfaces of brittle materials are susceptible to defects such as scratches,cracks,and chipping during con-ventional grinding processes,which significantly compromise surface quality and service performance.A flexi...The surfaces of brittle materials are susceptible to defects such as scratches,cracks,and chipping during con-ventional grinding processes,which significantly compromise surface quality and service performance.A flexible ball-end body-armor-like abrasive tool(BAAT)can effectively remove micro-convex peaks from the surfaces of brittle materials by employing a high tangential grinding force and a low normal grinding force,thereby achieving nano-level surface roughness and ultra-smooth mirror finishes.However,the surface contact me-chanism,pressure distribution pattern,and grinding force behavior between BAAT and workpiece remain in-adequately understood.This study examines the mechanism of liquid film formation and the distribution pattern of elastohydrodynamic pressure in high-shear and low-pressure grinding areas,drawing on the theories of elastohydrodynamic lubrication,non-Newtonian fluid dynamics,and material mechanics.A high-shear low-pressure grinding force model,which incorporates elastohydrodynamic liquid film thickness and abrasive grain size,was developed.The effects of the main grinding parameters(normal load,spindle rotational speed,and abrasive grain size)on the tangential grinding force were investigated through the processing of lithium niobate crystals using an intelligent precision-grinding system.The experimental results indicated that the relative error between the predicted and experimental values was 10.74%,thereby confirming the accuracy of the grinding force model.This study advances the understanding of elastohydrodynamic lubrication mechanisms in abrasive machining and provides a crucial theoretical foundation for the application of flexible ball-end BAAT.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
文摘近年来河流生态系统面临着水资源、流量调节不均的情况,生态流量是河流维持生态功能的重要指标。因此生态流量分析对于维持河流生态系统平衡与稳定、保护生物多样性以及提供各种生态系统服务具有重要意义。研究基于SWAT(Soil and Water Assessment Tool)模型,对海浪河流域进行了生态流量分析。构建天然径流与实际径流模型,分析土地利用变化对径流影响,结合Tennant法计算生态流量阈值,分析流域水量时空分布特征。对比不同水文年型下径流差异,并量化生态流量盈余空间格局。结果表明,天然径流与实际径流在年均分布和总量上存在差异,尤其是在汛期(5-9月),实际径流明显低于天然径流,而非汛期(10月至4月)变化较小;实际径流受到人类活动因素的影响,导致2、12、19、24号子流域水资源量略低于天然径流;由土地利用变化分析发现海浪河流域2005年土地利用模拟径流相较2000年径流下降4.25%,2010年土地利用模拟径流相较2005年径流下降1.56%,2015年土地利用模拟径流相较2010年径流上升0.99%。海浪河流域的生态流量需求均符合Tennant法中“最佳”标准等级的要求。生态流量盈余分析表明,流域水量在空间上呈现不均衡分布。研究提出需优化水资源调度策略以应对季节性供需失衡,为寒区河流生态流量管理提供模型融合分析范式。
基金supported by the National Key Research and Development Program of China(2022YFD1900501)the Gansu Provincial Water Conservancy Scientific Experimental Research and Technology Extension Project(25GSLK044,26GSLK093).
文摘Glacial meltwater constitutes a vital component of the water supply in arid and semi-arid areas.However,the influence of glacial melting on runoff and evapotranspiration under global warming remains insufficiently understood.Previous studies coupling the Soil and Water Assessment Tool(SWAT)model with glacier modules often failed to consider the spatial heterogeneity of temperature during glacial melting,potentially leading to biased estimates of meltwater volume.In this study,we developed a glacier-coupled SWAT(SWAT-glacier)model considering the digital elevation model(DEM)based temperature-driven glacial melt processes to elucidate the impact of glacial melting on hydrological processes across four river basins(Dongda,Xiying,Jinta,and Zamu)of the upper Shiyang River Basin(SYRB)in northwestern China from 1986 to 2021.Compared with the standard SWAT model,the proposed SWAT-glacier model significantly improved the simulation accuracy for both runoff and evapotranspiration.Specifically,in comparison with the standard SWAT model,the Nash-Sutcliffe efficiency of the SWAT-glacier model showed a relative improvement of approximately 0.42%–9.16%and 1.50%–10.15%for runoff and evapotranspiration,respectively,in the four river basins during the validation period.Annual glacial runoff occurred predominantly from May to October,whereas glacial melt-induced evapotranspiration peaked between June and August.From 1986 to 2021,the average contributions of glacial melt to runoff were 6.97%for Dongda,3.06%for Xiying,2.70%for Jinta,and 0.67%for Zamu,whereas its contributions to evapotranspiration were 9.06%,5.14%,3.21%,and 1.59%,respectively.This study presents a SWAT-glacier modeling framework that enhances the simulation of hydrological processes in cold regions.The proposed methodology can be extended to other glacierized basins to provide valuable insights into water resource management under climate change.
文摘水文模拟精度决定着流域水资源丰枯状态,开展水文模型不确定性研究可有效降低模拟结果的不确定性,从而提高径流组成成分模拟精度。为此,本文以汉江上游的子午河流域为例,提出一种改进的第二代非支配排序算法(Non-dominated Sorting Genetic Algorithm-Ⅱ,NSGA-Ⅱ)多目标算法校准分布式SWAT(Soil and Water Assessment Tool)模型,并将其与基于SWAT-CUP软件下SUFI-2算法的模拟结果进行对比分析。同时,采用Mann-Kendall趋势检验方法研究分析各子流域径流组成的趋势性演变特征。结果表明,改进的NSGA-Ⅱ算法能够较好地模拟子午河流域的水文过程,且模拟精度优于SUFI-2算法;其模拟的流域实际蒸发量、土壤蓄水量、总产水量和地下径流量精度均优于后者,而后者低估了各子流域的土壤蓄水量,尤其是地下径流量。研究结果对于流域水资源量的精准预估具有重要的指导意义。
文摘Soil erosion is a fundamental physical process driving land degradation across various spatial and temporal scales.The Soil and Water Assessment Tool(SWAT)model is a robust tool for predicting soil erosion and evaluating water and soil quality within watersheds.The latest version,SWAT+,introduces advanced encoding capabilities and improved performance,making it better suited for addressing complex watershed modeling challenges.This study implemented the SWAT+model to quantify soil erosion rates within the Chehelchay watershed in northern Iran.The foundational dataset comprises a 30-meter resolution Digital Elevation Model(DEM),land use classification,soil,and weather data.Model performance was evaluated using Nash-Sutcliffe Efficiency(NSE),coefficient of determination(R^(2)),root mean square error(RMSE),and percent bias(PBIAS).The SWAT+simulation revealed substantial spatial variation in erosion patterns across the watershed,with annual sediment yields in critical HRUs,reflecting diverse erosion intensities driven by variations in land use,soil characteristics,and slope.Among the Hydrological Response Units(HRUs),50 critical units,representing approximately 9%of the total watershed area,generate sediment yields exceeding 5 tons per hectare per year.The most severe erosion occurs predominantly in the central zone of the watershed.Downstream regions exhibit minimal soil loss due to gentle topography while upstream areas maintain soil stability through protective forest cover,resulting in negligible erosion rates.Best Management Practices(BMPs)were designed to safeguard water and soil resources at a watershed level.The study evaluated three strategic conservation interventions:alfalfa cultivation,agroforestry implementation,and garden development.When applied in combination,these measures achieved approximately 30%reduction in sediment yield at the HRU level.This integrated approach demonstrates the potential of combining multiple land management strategies to combat erosion effectively.
基金Supported by National Key R&D Program of China(Grant No.2023YFE0203800)the National Natural Science Foundation of China(Grant No.52105482).
文摘Off-axis aspherical mirrors are widely used in optical systems and precision measuring instruments,whereas off-axis aspherical mirrors with large sizes and off-axis are used in large optical systems such as astronomical telescopes and radio telescopes.However,if the off-axis amount of an off-axis aspherical mirror exceeds the capability of the machine tool,traditional rotary-turning machining methods are not applicable,and advanced computerized numerical control(CNC)machining methods,such as the slow-tool-servo method,must be im-plemented.This article proposes a non-conventional offset(NCO)fabrication method based on slow-tool-servo single-point diamond turning for machining off-axis aspherical surfaces with large off-axis amounts.This method is theoretically applicable to the machining of off-axis aspherical surfaces with any off-axis amount.NCO fab-rication is a simpler and more efficient path-planning solution for machining individual off-axis parabolic sur-faces.In addition,corresponding solutions for other types of aspherical surfaces are proposed using the NCO method.The turning depths of workpieces with different off-axis amounts at the same machining position are analyzed and compared.A specific measurement scheme for the NCO method is presented,and the experimental results indicate that the PV and RMS form errors are 0.658μm and 60 nm,respectively.This work demonstrates that the NCO method can effectively deal with the machining challenges of off-axis aspherical structures with large off-axis amounts.
基金supported by the project“Romanian Hub for Artificial Intelligence-HRIA”,Smart Growth,Digitization and Financial Instruments Program,2021–2027,MySMIS No.334906.
文摘Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.
基金Supported by National Natural Science Foundation of China(Grant Nos.52575516,51875329)Taishan Scholar Special Foundation of Shandong Province(Grant Nos.tstp20240826,tsqn201812064)+2 种基金Shandong Provincial Natural Science Foundation(Grant No.ZR2023ME112)Key Research and Development Project of the Ningxia Hui Autonomous Region(Grant No.2024BEE02019)Innovation Capacity Improvement Programme for High-tech SMEs of Shandong Province(Grant Nos.2022TSGC1333,2022TSGC1261).
文摘The surfaces of brittle materials are susceptible to defects such as scratches,cracks,and chipping during con-ventional grinding processes,which significantly compromise surface quality and service performance.A flexible ball-end body-armor-like abrasive tool(BAAT)can effectively remove micro-convex peaks from the surfaces of brittle materials by employing a high tangential grinding force and a low normal grinding force,thereby achieving nano-level surface roughness and ultra-smooth mirror finishes.However,the surface contact me-chanism,pressure distribution pattern,and grinding force behavior between BAAT and workpiece remain in-adequately understood.This study examines the mechanism of liquid film formation and the distribution pattern of elastohydrodynamic pressure in high-shear and low-pressure grinding areas,drawing on the theories of elastohydrodynamic lubrication,non-Newtonian fluid dynamics,and material mechanics.A high-shear low-pressure grinding force model,which incorporates elastohydrodynamic liquid film thickness and abrasive grain size,was developed.The effects of the main grinding parameters(normal load,spindle rotational speed,and abrasive grain size)on the tangential grinding force were investigated through the processing of lithium niobate crystals using an intelligent precision-grinding system.The experimental results indicated that the relative error between the predicted and experimental values was 10.74%,thereby confirming the accuracy of the grinding force model.This study advances the understanding of elastohydrodynamic lubrication mechanisms in abrasive machining and provides a crucial theoretical foundation for the application of flexible ball-end BAAT.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
