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
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
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
The changes in land use in the last 30 years in the territory of agro-forest watershed of Lake Buyo resulted in significant sediment into the lake. Sediments are a preferred means of transportation for certain polluta...The changes in land use in the last 30 years in the territory of agro-forest watershed of Lake Buyo resulted in significant sediment into the lake. Sediments are a preferred means of transportation for certain pollutants, like phosphorus in excess. By mapping the source areas of erosion, the authors can determine the risk areas and help to prioritize interventions in the territory. This mapping is done using the SWAT （soil and water assessment tool） model. Several types of data, including topography, land use, soil and climate data are needed to run the model. In this paper, all different steps are presented, from the designing of HRU （hydrological response units）, basic units to run the SWAT model until the simulations. The establishment of HRU has three main stages： space discretization, land use and soil data integration and HRU distribution： （1） space discretization which consist in extracting the limits and the water network of the watershed from the DEM （digital elevation model） and in subdividing them into sub-basins; （2） land use and soil data integration： it consists in digitizing the physical maps of land use and of soils under Mapinfo 7.5 and in keeping them in ＂shape＂ format; （3） HRU distribution： it leads to subdivide the sub-watersheds in small units that combine a single soil type and one type of land use. It appears from this study to obtain 23 sub-watersheds and 71 HRU. Once the HRU designed, it is necessary to integrate climate data, data on physico-chemical characteristics of soils and agricultural practices, before starting the simulations. This will allow the model to assess the risk of sedimentation and eutrophication of the lake using the MUSLE （modified universal soil loss equation） and phosphorus cycle.展开更多
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 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.
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.
文摘The changes in land use in the last 30 years in the territory of agro-forest watershed of Lake Buyo resulted in significant sediment into the lake. Sediments are a preferred means of transportation for certain pollutants, like phosphorus in excess. By mapping the source areas of erosion, the authors can determine the risk areas and help to prioritize interventions in the territory. This mapping is done using the SWAT （soil and water assessment tool） model. Several types of data, including topography, land use, soil and climate data are needed to run the model. In this paper, all different steps are presented, from the designing of HRU （hydrological response units）, basic units to run the SWAT model until the simulations. The establishment of HRU has three main stages： space discretization, land use and soil data integration and HRU distribution： （1） space discretization which consist in extracting the limits and the water network of the watershed from the DEM （digital elevation model） and in subdividing them into sub-basins; （2） land use and soil data integration： it consists in digitizing the physical maps of land use and of soils under Mapinfo 7.5 and in keeping them in ＂shape＂ format; （3） HRU distribution： it leads to subdivide the sub-watersheds in small units that combine a single soil type and one type of land use. It appears from this study to obtain 23 sub-watersheds and 71 HRU. Once the HRU designed, it is necessary to integrate climate data, data on physico-chemical characteristics of soils and agricultural practices, before starting the simulations. This will allow the model to assess the risk of sedimentation and eutrophication of the lake using the MUSLE （modified universal soil loss equation） and phosphorus cycle.
