VisualC++6.0入门:画笔与画刷在GDI绘图中的应用

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"轻松学 Visual C++ pdf" 在深入探讨“画笔和画刷-empirical asset pricing via machine learning”这一主题之前,我们首先需要理解Visual C++的基础知识,因为它是学习GDI绘图的重要背景。 Visual C++是微软公司推出的可视化C++开发工具,其名称暗示了它对软件开发领域的革新。在本书中,所有示例程序都是在VC++6.0集成开发环境(IDE)中完成的,尽管有更新的版本,但VC++6.0因其稳定性和广泛使用仍然受到青睐。 Visual C++包含了C++编译环境,这是一套用于处理C++源代码的工具,包括编辑器用于编写代码,调试器用于查找和修复错误,还有AppWizard和ClassWizard等辅助工具,帮助开发者快速创建和管理项目。这些组件集成在DeveloperStudio中,提供了一个无缝的开发体验。 C++是Visual C++的语言基础,由Bjarne Stroustrup博士在C语言基础上发展而来,引入了面向对象的特性。因此,开发者不仅可以使用Visual C++编写C++程序,也可以编写C语言程序,因为C++兼容C语言。 Windows API是Visual C++中的关键接口,它是一个庞大的函数库,允许程序员控制Windows操作系统的所有方面。在进行图形设备接口(GDI)编程时,Windows API提供了绘制线条、形状和填充图形所需的功能。画笔和画刷就是GDI中用于图形绘制的两个基本元素。 画笔(Pen)主要用于绘制线条和轮廓,它可以设置颜色、宽度和样式,从而影响线条的外观。例如,你可以用画笔绘制直线、曲线,或者定义一个形状的边界。而画刷(Brush)则用于填充封闭图形内部,比如矩形、椭圆或自定义路径。画刷同样可以设定颜色、图案和样式,决定填充区域的视觉效果。 在机器学习领域,"empirical asset pricing"可能指的是运用实证方法来分析资产定价。在GDI绘图的上下文中,这可能意味着使用数据驱动的方法来模拟或预测市场动态,并将结果以图形的形式呈现出来。通过结合Visual C++的GDI功能,开发者可以创建直观的金融图表,如股票价格走势,以帮助投资者理解和解释市场行为。 总结来说,"画笔和画刷"在GDI编程中是图形绘制的核心工具,它们与Visual C++的其他组件一起,为开发者提供了强大的图形表示能力。同时,"empirical asset pricing via machine learning"则提示我们将这些可视化技术应用于金融数据分析,以提升理解和决策效率。

Please revise the paper:Accurate determination of bathymetric data in the shallow water zone over time and space is of increasing significance for navigation safety, monitoring of sea-level uplift, coastal areas management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustics measurements over coastal areas with high spatial and temporal resolution combined with extensive repetitive coverage. Numerous empirical SDB approaches in previous works are unsuitable for precision bathymetry mapping in various scenarios, owing to the assumption of homogeneous bottom over the whole region, as well as the limitations of constructing global mapping relationships between water depth and blue-green reflectance takes no account of various confounding factors of radiance attenuation such as turbidity. To address the assumption failure of uniform bottom conditions and imperfect consideration of influence factors on the performance of the SDB model, this work proposes a bottom-type adaptive-based SDB approach (BA-SDB) to obtain accurate depth estimation over different sediments. The bottom type can be adaptively segmented by clustering based on bottom reflectance. For each sediment category, a PSO-LightGBM algorithm for depth derivation considering multiple influencing factors is driven to adaptively select the optimal influence factors and model parameters simultaneously. Water turbidity features beyond the traditional impact factors are incorporated in these regression models. Compared with log-ratio, multi-band and classical machine learning methods, the new approach produced the most accurate results with RMSE value is 0.85 m, in terms of different sediments and water depths combined with in-situ observations of airborne laser bathymetry and multi-beam echo sounder.

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