Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,an...Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,anti-jamming capabilities,and combat performance,making them critical for future warfare.However,varied perspectives in collaborative combat scenarios pose challenges to object detection,hindering traditional detection algorithms and reducing accuracy.Limited angle-prior data and sparse samples further complicate detection.This paper presents the Multi-View Collaborative Detection System,which tackles the challenges of multi-view object detection in collaborative combat scenarios.The system is designed to enhance multi-view image generation and detection algorithms,thereby improving the accuracy and efficiency of object detection across varying perspectives.First,an observation model for three-dimensional targets through line-of-sight angle transformation is constructed,and a multi-view image generation algorithm based on the Pix2Pix network is designed.For object detection,YOLOX is utilized,and a deep feature extraction network,BA-RepCSPDarknet,is developed to address challenges related to small target scale and feature extraction challenges.Additionally,a feature fusion network NS-PAFPN is developed to mitigate the issue of deep feature map information loss in UAV images.A visual attention module(BAM)is employed to manage appearance differences under varying angles,while a feature mapping module(DFM)prevents fine-grained feature loss.These advancements lead to the development of BA-YOLOX,a multi-view object detection network model suitable for drone platforms,enhancing accuracy and effectively targeting small objects.展开更多
The polyp dataset involves the confidentiality of medical records, so it might be difficult to obtain datasets with accurate annotations. This problem can be effectively solved by expanding the polyp data set with alg...The polyp dataset involves the confidentiality of medical records, so it might be difficult to obtain datasets with accurate annotations. This problem can be effectively solved by expanding the polyp data set with algorithms. The traditional polyp dataset expansion scheme usually requires the use of two models or traditional visual methods. These methods are both tedious and difficult to provide new polyp features for training data. Therefore, our research aims to efficiently generate high-quality polyp samples, so as to effectively expand the polyp dataset. In this study, we first added the attention mechanism to the generation model and improved the loss function to reduce the interference caused by reflection in the image generation process. Meanwhile, we used the improved generation model to remove polyps from the original image. In addition, we used masks of different shapes generated by random combinations to generate polyps with more characteristic information. The same generation model was used for the removal and generation of polyps. The generated polyp image has its own annotation, which is conducive to us directly using the expanded data set for training. Finally, we verified the effectiveness of the improved model and the dataset expansion scheme through a series of comparative experiments on the public dataset. The results showed that using the dataset we generate for training can significantly optimize the main performance indicators.展开更多
The increasing demand for radioauthorized applications in the 6G era necessitates enhanced monitoring and management of radio resources,particularly for precise control over the electromagnetic environment.The radio m...The increasing demand for radioauthorized applications in the 6G era necessitates enhanced monitoring and management of radio resources,particularly for precise control over the electromagnetic environment.The radio map serves as a crucial tool for describing signal strength distribution within the current electromagnetic environment.However,most existing algorithms rely on sparse measurements of radio strength,disregarding the impact of building information.In this paper,we propose a spectrum cartography(SC)algorithm that eliminates the need for relying on sparse ground-based radio strength measurements by utilizing a satellite network to collect data on buildings and transmitters.Our algorithm leverages Pix2Pix Generative Adversarial Network(GAN)to construct accurate radio maps using transmitter information within real geographical environments.Finally,simulation results demonstrate that our algorithm exhibits superior accuracy compared to previously proposed methods.展开更多
With the intensification of climate change,frequent short-duration heavy rainfall events exert significant impacts on human society and natural environment.Traditional rainfall recognition methods show limitations,inc...With the intensification of climate change,frequent short-duration heavy rainfall events exert significant impacts on human society and natural environment.Traditional rainfall recognition methods show limitations,including poor timeliness,inadequate handling of imbalanced data,and low accuracy when dealing with these events.This paper proposes a method based on CD-Pix2Pix model for inverting short-duration heavy rainfall events,aiming to improve the accuracy of inversion.The method integrates the attention mechanism network CSM-Net and the Dropblock module with a Bayesian optimized loss function to improve imbalanced data processing and enhance overall performance.This study utilizes multisource heterogeneous data,including radar composite reflectivity,FY-4B satellite data,and ground automatic station rainfall observations data,with China Meteorological Administration Land Data Assimilation System(CLDAS)data as the target labels fror the inversion task.Experimental results show that the enhanced method outperforms conventional rainfall inversion methods across multiple evaluation metrics,particularly demonstrating superior performance in Threat Score(TS,0.495),Probability of Detection(POD,0.857),and False Alarm Ratio(FAR,0.143).展开更多
Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcingsteel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling ...Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcingsteel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concreteis closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve theresolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. Torapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adoptedin this work, which is celebrated for its capabilities in image generation. The open-source dataset used hereinfeatures RGB images we generated using a sophisticated coupled model, while the grayscale images encapsulate the15 principal variables influencing spalling. After conducting a series of tests with different layers configurations,activation functions and loss functions, the Pix2Pix model suitable for assessing the spalling risk of fire-loadedconcrete has been meticulously designed and trained. The applicability and reliability of the Pix2Pix model inconcrete parameter prediction are verified by comparing its outcomes with those derived fromthe strong couplingTHC model. Notably, for the practical engineering applications, our findings indicate that utilizing monochromeimages as the initial target for analysis yields more dependable results. This work not only offers valuable insightsfor civil engineers specializing in concrete structures but also establishes a robust methodological approach forresearchers seeking to create similar predictive models.展开更多
Understanding metal accumulation at organ level in roots, leaves and seeds in O. glaberrima (OG) is crucial for improving physiological and metabolic aspects in growing Asian and African rice in salted areas. The micr...Understanding metal accumulation at organ level in roots, leaves and seeds in O. glaberrima (OG) is crucial for improving physiological and metabolic aspects in growing Asian and African rice in salted areas. The micro-analytical imaging techniques are required to reveal its accumulation and distribution within plant tissues. PIXE studies have been performed to determine different elements in rice plants. The existing microbeam analytical technique at the iThemba LABS will be applied for the 2D image mapping of fresh rice tissues to perform a concentration of low atomic mass elements (such as Al, Si, P, S, Cl, Ca, Ti, Mn, Fe, Cu, Br, Zn and K) with detection limits of typically 1-10 μg/g. Comparison of the distribution of the elements between leaves, root and seed samples using uptake and distribution of elements in particular environmental conditions with potential amount of salt in water have been performed. We are also expecting to indicate metal exclusion as salt tolerance strategies from leaves, root, and seed compartments using matrix correlation between samples and between elements on rice species.展开更多
基金supported by the Natural Science Foundation of China,Grant No.62103052.
文摘Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,anti-jamming capabilities,and combat performance,making them critical for future warfare.However,varied perspectives in collaborative combat scenarios pose challenges to object detection,hindering traditional detection algorithms and reducing accuracy.Limited angle-prior data and sparse samples further complicate detection.This paper presents the Multi-View Collaborative Detection System,which tackles the challenges of multi-view object detection in collaborative combat scenarios.The system is designed to enhance multi-view image generation and detection algorithms,thereby improving the accuracy and efficiency of object detection across varying perspectives.First,an observation model for three-dimensional targets through line-of-sight angle transformation is constructed,and a multi-view image generation algorithm based on the Pix2Pix network is designed.For object detection,YOLOX is utilized,and a deep feature extraction network,BA-RepCSPDarknet,is developed to address challenges related to small target scale and feature extraction challenges.Additionally,a feature fusion network NS-PAFPN is developed to mitigate the issue of deep feature map information loss in UAV images.A visual attention module(BAM)is employed to manage appearance differences under varying angles,while a feature mapping module(DFM)prevents fine-grained feature loss.These advancements lead to the development of BA-YOLOX,a multi-view object detection network model suitable for drone platforms,enhancing accuracy and effectively targeting small objects.
基金supported by the Natural Science Foundation Project of Fujian Province,China(Grant Nos.2023J011439 and 2019J01859).
文摘The polyp dataset involves the confidentiality of medical records, so it might be difficult to obtain datasets with accurate annotations. This problem can be effectively solved by expanding the polyp data set with algorithms. The traditional polyp dataset expansion scheme usually requires the use of two models or traditional visual methods. These methods are both tedious and difficult to provide new polyp features for training data. Therefore, our research aims to efficiently generate high-quality polyp samples, so as to effectively expand the polyp dataset. In this study, we first added the attention mechanism to the generation model and improved the loss function to reduce the interference caused by reflection in the image generation process. Meanwhile, we used the improved generation model to remove polyps from the original image. In addition, we used masks of different shapes generated by random combinations to generate polyps with more characteristic information. The same generation model was used for the removal and generation of polyps. The generated polyp image has its own annotation, which is conducive to us directly using the expanded data set for training. Finally, we verified the effectiveness of the improved model and the dataset expansion scheme through a series of comparative experiments on the public dataset. The results showed that using the dataset we generate for training can significantly optimize the main performance indicators.
文摘The increasing demand for radioauthorized applications in the 6G era necessitates enhanced monitoring and management of radio resources,particularly for precise control over the electromagnetic environment.The radio map serves as a crucial tool for describing signal strength distribution within the current electromagnetic environment.However,most existing algorithms rely on sparse measurements of radio strength,disregarding the impact of building information.In this paper,we propose a spectrum cartography(SC)algorithm that eliminates the need for relying on sparse ground-based radio strength measurements by utilizing a satellite network to collect data on buildings and transmitters.Our algorithm leverages Pix2Pix Generative Adversarial Network(GAN)to construct accurate radio maps using transmitter information within real geographical environments.Finally,simulation results demonstrate that our algorithm exhibits superior accuracy compared to previously proposed methods.
基金Key Project of the NSFC Joint Fund(U20B2061)Innovation Development Special Project(CXFZ2024J001,CXFZ2023J013)+3 种基金Key Open Fund of the Laboratory of Hydrometeorology,China Meteorological Administration(23SWQXZ001)Open Research Fund of Anyang National Climate Observatory(AYNCOF202401)Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX24_0478)Zhejiang Provincial Natural Science Foundation Project(LZJMD25D050002)。
文摘With the intensification of climate change,frequent short-duration heavy rainfall events exert significant impacts on human society and natural environment.Traditional rainfall recognition methods show limitations,including poor timeliness,inadequate handling of imbalanced data,and low accuracy when dealing with these events.This paper proposes a method based on CD-Pix2Pix model for inverting short-duration heavy rainfall events,aiming to improve the accuracy of inversion.The method integrates the attention mechanism network CSM-Net and the Dropblock module with a Bayesian optimized loss function to improve imbalanced data processing and enhance overall performance.This study utilizes multisource heterogeneous data,including radar composite reflectivity,FY-4B satellite data,and ground automatic station rainfall observations data,with China Meteorological Administration Land Data Assimilation System(CLDAS)data as the target labels fror the inversion task.Experimental results show that the enhanced method outperforms conventional rainfall inversion methods across multiple evaluation metrics,particularly demonstrating superior performance in Threat Score(TS,0.495),Probability of Detection(POD,0.857),and False Alarm Ratio(FAR,0.143).
基金the National Natural Science Foundation of China(NSFC)(52178324).
文摘Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcingsteel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concreteis closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve theresolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. Torapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adoptedin this work, which is celebrated for its capabilities in image generation. The open-source dataset used hereinfeatures RGB images we generated using a sophisticated coupled model, while the grayscale images encapsulate the15 principal variables influencing spalling. After conducting a series of tests with different layers configurations,activation functions and loss functions, the Pix2Pix model suitable for assessing the spalling risk of fire-loadedconcrete has been meticulously designed and trained. The applicability and reliability of the Pix2Pix model inconcrete parameter prediction are verified by comparing its outcomes with those derived fromthe strong couplingTHC model. Notably, for the practical engineering applications, our findings indicate that utilizing monochromeimages as the initial target for analysis yields more dependable results. This work not only offers valuable insightsfor civil engineers specializing in concrete structures but also establishes a robust methodological approach forresearchers seeking to create similar predictive models.
文摘Understanding metal accumulation at organ level in roots, leaves and seeds in O. glaberrima (OG) is crucial for improving physiological and metabolic aspects in growing Asian and African rice in salted areas. The micro-analytical imaging techniques are required to reveal its accumulation and distribution within plant tissues. PIXE studies have been performed to determine different elements in rice plants. The existing microbeam analytical technique at the iThemba LABS will be applied for the 2D image mapping of fresh rice tissues to perform a concentration of low atomic mass elements (such as Al, Si, P, S, Cl, Ca, Ti, Mn, Fe, Cu, Br, Zn and K) with detection limits of typically 1-10 μg/g. Comparison of the distribution of the elements between leaves, root and seed samples using uptake and distribution of elements in particular environmental conditions with potential amount of salt in water have been performed. We are also expecting to indicate metal exclusion as salt tolerance strategies from leaves, root, and seed compartments using matrix correlation between samples and between elements on rice species.
