VisualC++6.0与多线程编程入门

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"进程与线程-empirical asset pricing via machine learning" 本文主要介绍的是进程与线程的概念,特别是在Windows操作系统中的应用,以及如何通过Visual C++进行多线程编程。进程是操作系统分配资源的基本单位,每个进程都有独立的内存空间,可以包含一个或多个线程。线程则是执行的基本单元,它是共享同一进程资源的轻量级实体,允许在同一程序中并发执行多个任务,从而提高系统资源利用率和用户效率。 在Windows操作系统中,多任务处理能力使得用户能够同时运行多个应用程序,而多线程技术则允许单个程序内并行执行多个不同的任务。通过熟练掌握线程的创建、管理和同步,开发者可以编写出高效的多线程程序,提升软件性能。 Visual C++ 是微软提供的一个强大的C++集成开发环境,特别是指VC++6.0版本,尽管有更新的版本发布,但其依旧被广泛使用。Visual C++不仅包括C++编译器,还包含微软基础类库(MFC)和SDK Platform,提供了一个完整的开发工具集。其中,MFC库是基于Windows API的,用于简化Windows应用程序的开发,提供了丰富的类来封装Windows API函数,使得开发者能够更容易地构建用户界面和处理系统事件。 开发环境中的关键组件如编辑器、调试器、AppWizard和ClassWizard等,为开发者提供了便捷的代码编写、编译、调试以及项目创建和管理功能。AppWizard帮助快速生成项目框架,ClassWizard则协助处理面向对象编程中的类定义和成员函数的添加。 C++语言是Visual C++的基础,它扩展了C语言,引入了面向对象编程特性,如类、继承、封装和多态性。因此,在Visual C++环境下,既可以编写传统的C语言程序,也可以进行复杂的C++对象导向编程。 Windows API是Windows操作系统的核心接口,包含了大量用于控制和交互的函数,开发者可以通过调用这些函数实现与系统的深度交互,如窗口管理、文件操作、网络通信等。在多线程编程中,Windows API提供了如CreateThread、WaitForSingleObject等函数,用于创建线程和同步线程间的操作,确保数据安全和程序的正确执行。 总结来说,本章内容深入浅出地介绍了进程与线程的概念,以及如何在Windows环境中利用Visual C++进行多线程编程,通过学习可以提升开发者在Windows平台上的软件开发技能,尤其是利用C++实现高效的多任务和多线程程序设计。

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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