In this paper, we study some new systems of generalized quasi-variational inclusion problems in FC-spaces without convexity structure.By applying an existence theorem of maximal elements of set-valued mappings due to ...In this paper, we study some new systems of generalized quasi-variational inclusion problems in FC-spaces without convexity structure.By applying an existence theorem of maximal elements of set-valued mappings due to the author, some new existence theorems of solutions for the systems of generalized quasi-variational inclusion problems are proved in noncompact FC-spaces. As applications, some existence results of solutions for the system of quasi-optimization problems and mathematical programs with the systems of generalized quasi-variational inclusion constraints are obtained in FC-spaces.展开更多
随着航天器数目的增加,仅利用传统的地基测控与通信(Tracking,Telemetry and Command,TT&C)网对成百上千卫星火箭等航天器目标同时进行跟踪测量,将面临测控资源紧缺的难题,无法满足高精度导航的需求,而扩充地基测控资源的建设和维...随着航天器数目的增加,仅利用传统的地基测控与通信(Tracking,Telemetry and Command,TT&C)网对成百上千卫星火箭等航天器目标同时进行跟踪测量,将面临测控资源紧缺的难题,无法满足高精度导航的需求,而扩充地基测控资源的建设和维护成本又非常高。全球卫星导航系统(Global Navigation Satellite System,GNSS)通过提供高精度定位、导航和授时(Positioning Navigation and Timing,PNT)服务,已成为航天测控不可或缺的技术手段,作为地基测控网的有益补充,可以在不大幅增加任务成本的前提下,进一步提升空间飞行器导航的可靠性和精度。探讨了GNSS在航天测控系统中的应用,分析了其面临的机遇与挑战。梳理了GNSS在近地空间航天器测控、地月空间及深远空间航天器测控和地面测控站中的应用进展情况。进一步提出了GNSS支持航天测控领域的重点难点和关键技术,包括时空基准传递和统一技术、高灵敏度接收机和高增益天线技术、海量多源异构数据弹性自适应融合处理技术等,并给出了相应的启示和建议,为GNSS在航天测控领域的进一步开发利用提供了参考。展开更多
This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot ...This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot taken from a top-down perspective at a distance of 30 cm. These samples were projected into the CIELAB color space, and the extracted pixels were plotted on the a*b* plane, excluding the luminance value. A polygon was then drawn around all the plotted pixels, defining the color to be identified. Subsequently, the application analyzed another image to determine the number of pixels within the polygon. These identified pixels were transformed to white, and the percentage of these pixels relative to the total number of pixels in the image was calculated. This process yielded percentages for brown (soil), green (leaf cover), and pink (stem color). A single polygon was sufficient to accurately identify the green and brown colors in the images. However, due to varying lighting conditions, customized polygons were necessary for each image to accurately identify the stem color. To validate the green polygon’s accuracy in identifying purslane leaves, all leaves in the image were digitized in AutoCADTM, and the green area was compared to the total image area to obtain the observed green percentage. The green percentage obtained with the polygon was then compared to the observed green percentage, resulting in an R2 value of 0.8431. Similarly, for the brown color, an R2 value of 0.9305 was found. The stem color was not subjected to this validation due to the necessity of multiple polygons. The R2 values were derived from percentage data obtained by analyzing the total pixels in the images. When sampling to estimate the proportion and analyzing only the suggested sample size of pixels, R2 values of 0.93049 for brown and 0.8088 for green were obtained. The average analysis time to determine the brown soil percentage using the polygon (BP) for 26 images with an average size of 1070 × 1210 pixels was 44 seconds. In contrast, sampling to estimate the proportion reduced the analysis time to 0.9 seconds for the same number of images. This indicates that significant time savings can be achieved while obtaining similar results.展开更多
基金supported by the Scientific Research Fun of Sichuan Normal University(09ZDL04)the Sichuan Province Leading Academic Discipline Project(SZD0406)
文摘In this paper, we study some new systems of generalized quasi-variational inclusion problems in FC-spaces without convexity structure.By applying an existence theorem of maximal elements of set-valued mappings due to the author, some new existence theorems of solutions for the systems of generalized quasi-variational inclusion problems are proved in noncompact FC-spaces. As applications, some existence results of solutions for the system of quasi-optimization problems and mathematical programs with the systems of generalized quasi-variational inclusion constraints are obtained in FC-spaces.
文摘随着航天器数目的增加,仅利用传统的地基测控与通信(Tracking,Telemetry and Command,TT&C)网对成百上千卫星火箭等航天器目标同时进行跟踪测量,将面临测控资源紧缺的难题,无法满足高精度导航的需求,而扩充地基测控资源的建设和维护成本又非常高。全球卫星导航系统(Global Navigation Satellite System,GNSS)通过提供高精度定位、导航和授时(Positioning Navigation and Timing,PNT)服务,已成为航天测控不可或缺的技术手段,作为地基测控网的有益补充,可以在不大幅增加任务成本的前提下,进一步提升空间飞行器导航的可靠性和精度。探讨了GNSS在航天测控系统中的应用,分析了其面临的机遇与挑战。梳理了GNSS在近地空间航天器测控、地月空间及深远空间航天器测控和地面测控站中的应用进展情况。进一步提出了GNSS支持航天测控领域的重点难点和关键技术,包括时空基准传递和统一技术、高灵敏度接收机和高增益天线技术、海量多源异构数据弹性自适应融合处理技术等,并给出了相应的启示和建议,为GNSS在航天测控领域的进一步开发利用提供了参考。
文摘This study utilized a computer application developed in Visual StudioTM using C# to extract pixel samples (RGB) from multiple images (26 images obtained from August 20, 2024, to September 22, 2024), of a purslane pot taken from a top-down perspective at a distance of 30 cm. These samples were projected into the CIELAB color space, and the extracted pixels were plotted on the a*b* plane, excluding the luminance value. A polygon was then drawn around all the plotted pixels, defining the color to be identified. Subsequently, the application analyzed another image to determine the number of pixels within the polygon. These identified pixels were transformed to white, and the percentage of these pixels relative to the total number of pixels in the image was calculated. This process yielded percentages for brown (soil), green (leaf cover), and pink (stem color). A single polygon was sufficient to accurately identify the green and brown colors in the images. However, due to varying lighting conditions, customized polygons were necessary for each image to accurately identify the stem color. To validate the green polygon’s accuracy in identifying purslane leaves, all leaves in the image were digitized in AutoCADTM, and the green area was compared to the total image area to obtain the observed green percentage. The green percentage obtained with the polygon was then compared to the observed green percentage, resulting in an R2 value of 0.8431. Similarly, for the brown color, an R2 value of 0.9305 was found. The stem color was not subjected to this validation due to the necessity of multiple polygons. The R2 values were derived from percentage data obtained by analyzing the total pixels in the images. When sampling to estimate the proportion and analyzing only the suggested sample size of pixels, R2 values of 0.93049 for brown and 0.8088 for green were obtained. The average analysis time to determine the brown soil percentage using the polygon (BP) for 26 images with an average size of 1070 × 1210 pixels was 44 seconds. In contrast, sampling to estimate the proportion reduced the analysis time to 0.9 seconds for the same number of images. This indicates that significant time savings can be achieved while obtaining similar results.
