Treatment of peat soil foundation in Yunnan surrounding Dianchi and Erhai Lakes poses complex problems for engineering projects.It is insufficient to rely on ordinary cement to reinforce peat soil.In order to make the...Treatment of peat soil foundation in Yunnan surrounding Dianchi and Erhai Lakes poses complex problems for engineering projects.It is insufficient to rely on ordinary cement to reinforce peat soil.In order to make the reinforcement reliable,this experiment mixed(ultrafine cement)UFC into ordinary cement to form a composite solidify agent.This study aimed to analyze the influence of UFC proportion on the strength of cement-soil in the peat soil environment.Unconfined compressive strength(UCS)and scanning electron microscope(SEM)tests were conducted on samples soaked for 28 and 90 days,respectively.The test results show that without considering the effects of Humic Acid(HA)and Fulvic Acid(FA),incorporating UFC can significantly improve the UCS of cement-soil.The rapid hydration of the fine particles generates a large number of cementitious products,improves the cohesion of the soil skeleton,and fills the pores.However,when the proportion of UFC increases,the aggregate structure formed by a larger quantity of fine particles reduces the hydration rate and degree of cement hydration,making the UCS growth rate of cement-soil insignificant.In the peat soil environment,HA significantly weakened the UCS of cement-soil in both physical and chemical aspects.However,UFC can mitigate the adverse effect of HA on cement-soil by its small particle size,high surface energy,and solid binding ability.In addition,FA has a positive effect on the UCS of cement-soil soaked for 28 days and 90 days.The UFC addition could promote the enhancement effect of FA on cement-soil UCS.SEM test results showed that cement hydration products increased significantly with the increase of UFC proportion,and cementation between hydration products and soil particles was enhanced.The size and connectivity of cement-soil pores were significantly reduced,thereby improving cement-soil structural integrity.展开更多
To compare the suitable working conditions of polypropylene(PP)and polycaprolactam(PA6)materials in actual use in automobiles,the effects of different temperature aging and different reagents on the mechanical propert...To compare the suitable working conditions of polypropylene(PP)and polycaprolactam(PA6)materials in actual use in automobiles,the effects of different temperature aging and different reagents on the mechanical properties of the two materials,such as tensile,bending,compression,and impact were studied.The results indicate that the short⁃term low⁃temperature environment had no much effect on the mechanical properties of PP and PA6.After long⁃term thermal aging at 80℃,the strength of PP and PA6 increased while their toughness decreased.After short⁃term thermal aging at 120℃,PP strength decreases and toughness increases,while PA6 strength increases and toughness decreases.The soaking of glass water and car shampoo had no much effect on the mechanical properties of PP,but had a significant impact on the mechanical properties of PA6.With the increase of soaking time,the strength of PA6 significantly decreases and the toughness significantly increases.The soaking of 95#gasoline had no much effect on the mechanical properties of PA6,but has a significant impact on the mechanical properties of PP.After 720 h of soaking,the retention rates of the tensile strength,bending strength,and compressive strength of PP were all less than 80%,while the retention rate of the impact strength of the cantilever beam was 160.4%.展开更多
Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threat...Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threaten flight safety and mission success.Traditional path planning methods typically depend solely on the distribution of static obstacles to generate collision-free paths,without accounting for constraints imposed by enemy detection and strike capabilities.Such a simplified approach can yield safety-compromising routes in highly complex urban airspace.To address these limitations,this study proposes a multi-parameter path planning method based on reachable airspace visibility graphs,which integrates UAV performance constraints,environmental limitations,and exposure risks.An innovative heuristic algorithm is developed to balance operational safety and efficiency by both exposure risks and path length.In the case study set in a typical mixed-use urban area,analysis of airspace visibility graphs reveals significant variations in exposure risk at different regions and altitudes due to building encroachments.Path optimization results indicate that the method can effectively generate covert and efficient flight paths by dynamically adjusting the exposure index,which represents the likelihood of enemy detection,and the path length,which corresponds to mission execution time.展开更多
This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy ...This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.展开更多
The ocean is one of the essential fields of national defense in the future,and more and more attention is paid to the lightweight research of Marine equipment and materials.This study it is to develop a Machine learni...The ocean is one of the essential fields of national defense in the future,and more and more attention is paid to the lightweight research of Marine equipment and materials.This study it is to develop a Machine learning(ML)-based prediction method to study the evolution of the mechanical properties of Al-Li alloys in the marine environment.We obtained the mechanical properties of Al-Li alloy samples under uniaxial tensile deformation at different exposure times through Marine exposure experiments.We obtained the strain evolution by digital image correlation(DIC).The strain field images are voxelized using 2D-Convolutional Neural Networks(CNN)autoencoders as input data for Long Short-Term Memory(LSTM)neural networks.Then,the output data of LSTM neural networks combined with corrosion features were input into the Back Propagation(BP)neural network to predict the mechanical properties of Al-Li alloys.The main conclusions are as follows:1.The variation law of mechanical properties of2297-T8 in the Marine atmosphere is revealed.With the increase in outdoor exposure test time,the tensile elastic model of 2297-T8 changes slowly,within 10%,and the tensile yield stress changes significantly,with a maximum attenuation of 23.6%.2.The prediction model can predict the strain evolution and mechanical response simultaneously with an error of less than 5%.3.This study shows that a CNN/LSTM system based on machine learning can be built to capture the corrosion characteristics of Marine exposure experiments.The results show that the relationship between corrosion characteristics and mechanical response can be predicted without considering the microstructure evolution of metal materials.展开更多
For regional ecological management,it is important to evaluate the quality of ecosystems and analyze the underlying causes of ecological changes.Using the Google Earth Engine(GEE)platform,the remote sensing ecological...For regional ecological management,it is important to evaluate the quality of ecosystems and analyze the underlying causes of ecological changes.Using the Google Earth Engine(GEE)platform,the remote sensing ecological index(RSEI)was calculated for the Lijiang River Basin in Guangxi Zhuang Autonomous Region for 1991,2001,2011,and 2021.Spatial autocorrelation analysis was employed to investigate spatiotemporal variations in the ecological environmental quality of the Lijiang River Basin.Furthermore,geographic detectors were used to quantitatively analyze influencing factors and their interaction effects on ecological environmental quality.The results verified that:1)From 1991 to 2021,the ecological environmental quality of the Lijiang River Basin demonstrated significant improvement.The area with good and excellent ecological environmental quality in proportion increased by 19.69%(3406.57 km^(2)),while the area with fair and poor ecological environmental quality in proportion decreased by 10.76%(1860.36 km^(2)).2)Spatially,the ecological environmental quality of the Lijiang River Basin exhibited a pattern of low quality in the central region and high quality in the periphery.Specifically,poor ecological environmental quality characterized the Guilin urban area,Pingle County,and Lingchuan County.3)From 1991 to 2021,a significant positive spatial correlation was observed in ecological environmental quality of the Lijiang River Basin.Areas with high-high agglomeration were predominantly forests and grasslands,indicating good ecological environmental quality,whereas areas with low-low agglomeration were dominated by cultivated land and construction land,indicating poor ecological environmental quality.4)Annual average precipitation and temperature exerted the most significant influence on the ecological environmental quality of the basin,and their interactions with other factors had the great influence.This study aimed to enhance understanding of the evolution of the ecological environment in the Lijiang River Basin of Guangxi Zhuang Autonomous Region and provide scientific guidance for decision-making and management related to ecology in the region.展开更多
Urea,paracetamol and glutamine(based on the expired drugs)were selected as vapor-phase corrosion inhibitors(VCIs)to study their corrosion protection effect on red copper in simulated marine atmospheric environment by ...Urea,paracetamol and glutamine(based on the expired drugs)were selected as vapor-phase corrosion inhibitors(VCIs)to study their corrosion protection effect on red copper in simulated marine atmospheric environment by using weight loss,electrochemical measurement techniques(specially designed electrochemical testing device for simulating marine atmospheric environments)and surface morphology characterization analysis(SEM/EDS,XRD,RAMAN,XPS).Weight loss results show that the three corrosion inhibitors have good corrosion inhibition effect on red copper,and the corrosion inhibition efficiency in the order of glutamine(83.62%)>urea(68.46%)>paracetamol(61.47%).Surface morphology characterization analysis provides evidence of adsorption of corrosion inhibitors molecules on the red copper surface,thus forming a protective film that blocked the red copper surface from the aggressive chloride ion attack.展开更多
With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the netw...With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the network has risen sharply.Due to the high cost of edge computing resources,coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge.In this paper,we focus on optimiz-ing the placement of network services in cloud-edge environ-ments to maximize the efficiency.It is first proved that,in cloud-edge environments,placing one service function chain(SFC)integrally in the cloud or at the edge can improve the utilization efficiency of edge resources.Then a virtual network function(VNF)performance-resource(P-R)function is proposed to repre-sent the relationship between the VNF instance computing per-formance and the allocated computing resource.To select the SFCs that are most suitable to deploy at the edge,a VNF place-ment and resource allocation model is built to configure each VNF with its particular P-R function.Moreover,a heuristic recur-sive algorithm is designed called the recursive algorithm for max edge throughput(RMET)to solve the model.Through simula-tions on two scenarios,it is verified that RMET can improve the utilization efficiency of edge computing resources.展开更多
基金National Natural Science Foundation of China(No.41967035)。
文摘Treatment of peat soil foundation in Yunnan surrounding Dianchi and Erhai Lakes poses complex problems for engineering projects.It is insufficient to rely on ordinary cement to reinforce peat soil.In order to make the reinforcement reliable,this experiment mixed(ultrafine cement)UFC into ordinary cement to form a composite solidify agent.This study aimed to analyze the influence of UFC proportion on the strength of cement-soil in the peat soil environment.Unconfined compressive strength(UCS)and scanning electron microscope(SEM)tests were conducted on samples soaked for 28 and 90 days,respectively.The test results show that without considering the effects of Humic Acid(HA)and Fulvic Acid(FA),incorporating UFC can significantly improve the UCS of cement-soil.The rapid hydration of the fine particles generates a large number of cementitious products,improves the cohesion of the soil skeleton,and fills the pores.However,when the proportion of UFC increases,the aggregate structure formed by a larger quantity of fine particles reduces the hydration rate and degree of cement hydration,making the UCS growth rate of cement-soil insignificant.In the peat soil environment,HA significantly weakened the UCS of cement-soil in both physical and chemical aspects.However,UFC can mitigate the adverse effect of HA on cement-soil by its small particle size,high surface energy,and solid binding ability.In addition,FA has a positive effect on the UCS of cement-soil soaked for 28 days and 90 days.The UFC addition could promote the enhancement effect of FA on cement-soil UCS.SEM test results showed that cement hydration products increased significantly with the increase of UFC proportion,and cementation between hydration products and soil particles was enhanced.The size and connectivity of cement-soil pores were significantly reduced,thereby improving cement-soil structural integrity.
文摘To compare the suitable working conditions of polypropylene(PP)and polycaprolactam(PA6)materials in actual use in automobiles,the effects of different temperature aging and different reagents on the mechanical properties of the two materials,such as tensile,bending,compression,and impact were studied.The results indicate that the short⁃term low⁃temperature environment had no much effect on the mechanical properties of PP and PA6.After long⁃term thermal aging at 80℃,the strength of PP and PA6 increased while their toughness decreased.After short⁃term thermal aging at 120℃,PP strength decreases and toughness increases,while PA6 strength increases and toughness decreases.The soaking of glass water and car shampoo had no much effect on the mechanical properties of PP,but had a significant impact on the mechanical properties of PA6.With the increase of soaking time,the strength of PA6 significantly decreases and the toughness significantly increases.The soaking of 95#gasoline had no much effect on the mechanical properties of PA6,but has a significant impact on the mechanical properties of PP.After 720 h of soaking,the retention rates of the tensile strength,bending strength,and compressive strength of PP were all less than 80%,while the retention rate of the impact strength of the cantilever beam was 160.4%.
基金supported by the Ministry of Industry and Information Technology(No.23100002022102001)。
文摘Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threaten flight safety and mission success.Traditional path planning methods typically depend solely on the distribution of static obstacles to generate collision-free paths,without accounting for constraints imposed by enemy detection and strike capabilities.Such a simplified approach can yield safety-compromising routes in highly complex urban airspace.To address these limitations,this study proposes a multi-parameter path planning method based on reachable airspace visibility graphs,which integrates UAV performance constraints,environmental limitations,and exposure risks.An innovative heuristic algorithm is developed to balance operational safety and efficiency by both exposure risks and path length.In the case study set in a typical mixed-use urban area,analysis of airspace visibility graphs reveals significant variations in exposure risk at different regions and altitudes due to building encroachments.Path optimization results indicate that the method can effectively generate covert and efficient flight paths by dynamically adjusting the exposure index,which represents the likelihood of enemy detection,and the path length,which corresponds to mission execution time.
基金The National Natural Science Foundation of China (32371993)The Natural Science Research Key Project of Anhui Provincial University(2022AH040125&2023AH040135)The Key Research and Development Plan of Anhui Province (202204c06020022&2023n06020057)。
文摘This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.
基金supported by the Southwest Institute of Technology and Engineering cooperation fund(Grant No.HDHDW5902020104)。
文摘The ocean is one of the essential fields of national defense in the future,and more and more attention is paid to the lightweight research of Marine equipment and materials.This study it is to develop a Machine learning(ML)-based prediction method to study the evolution of the mechanical properties of Al-Li alloys in the marine environment.We obtained the mechanical properties of Al-Li alloy samples under uniaxial tensile deformation at different exposure times through Marine exposure experiments.We obtained the strain evolution by digital image correlation(DIC).The strain field images are voxelized using 2D-Convolutional Neural Networks(CNN)autoencoders as input data for Long Short-Term Memory(LSTM)neural networks.Then,the output data of LSTM neural networks combined with corrosion features were input into the Back Propagation(BP)neural network to predict the mechanical properties of Al-Li alloys.The main conclusions are as follows:1.The variation law of mechanical properties of2297-T8 in the Marine atmosphere is revealed.With the increase in outdoor exposure test time,the tensile elastic model of 2297-T8 changes slowly,within 10%,and the tensile yield stress changes significantly,with a maximum attenuation of 23.6%.2.The prediction model can predict the strain evolution and mechanical response simultaneously with an error of less than 5%.3.This study shows that a CNN/LSTM system based on machine learning can be built to capture the corrosion characteristics of Marine exposure experiments.The results show that the relationship between corrosion characteristics and mechanical response can be predicted without considering the microstructure evolution of metal materials.
基金supported by the Guangxi Natural Science Foundation(2020GXNSFAA297266)Doctoral Research Foundation of Guilin University of Technology(GUTQDJJ2007059)Guangxi Hidden Metallic Mineral Exploration Key Laboratory。
文摘For regional ecological management,it is important to evaluate the quality of ecosystems and analyze the underlying causes of ecological changes.Using the Google Earth Engine(GEE)platform,the remote sensing ecological index(RSEI)was calculated for the Lijiang River Basin in Guangxi Zhuang Autonomous Region for 1991,2001,2011,and 2021.Spatial autocorrelation analysis was employed to investigate spatiotemporal variations in the ecological environmental quality of the Lijiang River Basin.Furthermore,geographic detectors were used to quantitatively analyze influencing factors and their interaction effects on ecological environmental quality.The results verified that:1)From 1991 to 2021,the ecological environmental quality of the Lijiang River Basin demonstrated significant improvement.The area with good and excellent ecological environmental quality in proportion increased by 19.69%(3406.57 km^(2)),while the area with fair and poor ecological environmental quality in proportion decreased by 10.76%(1860.36 km^(2)).2)Spatially,the ecological environmental quality of the Lijiang River Basin exhibited a pattern of low quality in the central region and high quality in the periphery.Specifically,poor ecological environmental quality characterized the Guilin urban area,Pingle County,and Lingchuan County.3)From 1991 to 2021,a significant positive spatial correlation was observed in ecological environmental quality of the Lijiang River Basin.Areas with high-high agglomeration were predominantly forests and grasslands,indicating good ecological environmental quality,whereas areas with low-low agglomeration were dominated by cultivated land and construction land,indicating poor ecological environmental quality.4)Annual average precipitation and temperature exerted the most significant influence on the ecological environmental quality of the basin,and their interactions with other factors had the great influence.This study aimed to enhance understanding of the evolution of the ecological environment in the Lijiang River Basin of Guangxi Zhuang Autonomous Region and provide scientific guidance for decision-making and management related to ecology in the region.
基金Project(ZR2023ME063)supported by the Shandong Provincial Natural Science Foundation,ChinaProject(121311KYSB20210005)supported by the Overseas Science and Education Cooperation Center Deployment Project,ChinaProject supported by the Qingdao Expert Workstation for Intelligent Anticorrosion for Water Diversion Project,China。
文摘Urea,paracetamol and glutamine(based on the expired drugs)were selected as vapor-phase corrosion inhibitors(VCIs)to study their corrosion protection effect on red copper in simulated marine atmospheric environment by using weight loss,electrochemical measurement techniques(specially designed electrochemical testing device for simulating marine atmospheric environments)and surface morphology characterization analysis(SEM/EDS,XRD,RAMAN,XPS).Weight loss results show that the three corrosion inhibitors have good corrosion inhibition effect on red copper,and the corrosion inhibition efficiency in the order of glutamine(83.62%)>urea(68.46%)>paracetamol(61.47%).Surface morphology characterization analysis provides evidence of adsorption of corrosion inhibitors molecules on the red copper surface,thus forming a protective film that blocked the red copper surface from the aggressive chloride ion attack.
基金This work was supported by the Key Research and Development(R&D)Plan of Heilongjiang Province of China(JD22A001).
文摘With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the network has risen sharply.Due to the high cost of edge computing resources,coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge.In this paper,we focus on optimiz-ing the placement of network services in cloud-edge environ-ments to maximize the efficiency.It is first proved that,in cloud-edge environments,placing one service function chain(SFC)integrally in the cloud or at the edge can improve the utilization efficiency of edge resources.Then a virtual network function(VNF)performance-resource(P-R)function is proposed to repre-sent the relationship between the VNF instance computing per-formance and the allocated computing resource.To select the SFCs that are most suitable to deploy at the edge,a VNF place-ment and resource allocation model is built to configure each VNF with its particular P-R function.Moreover,a heuristic recur-sive algorithm is designed called the recursive algorithm for max edge throughput(RMET)to solve the model.Through simula-tions on two scenarios,it is verified that RMET can improve the utilization efficiency of edge computing resources.
