Irrlicht-1.7.1 Tutorial Translation

本文回忆了作者初次接触著名跨平台3D引擎Irrlicht的经历,从源码包的解压到使用VS2008打开解决方案,面对众多源文件的迷茫与探索。通过阅读教程代码、调试、追踪并修改示例代码,逐步掌握了Irrlicht的使用方法。文中还分享了部分教程的中文翻译,为中文读者提供了学习资源。

When I clean the materials in my computer, I found some tutorial translations about Irrlicht-1.7.1 that is a very famous and cross platform 3D engine. Those things remember me that days when I was the first time touched the Irrlicht engine, Unzip the source package, open the solution with VS2008. Oh, so many source files, where I should start if I want to become a user of Irrlicht? Read the source file one by one, or find some Irrlicht Bibles on the Internet and read them one by one Or any other ways?

I was very eagles to master the Irrlicht in a very shot time, but at the same time I knew that I could not manage to that. So I came down, read the tutorial code, debug, trace and modify the demo code one by one. After I went for certain time, I felt that I could connect all those engine features well and understand why they did like this. To write what I understand about Irrlicht, I decided to translate some part of the Irrlicht tutorials. Here I translated tutorial 01 to 16, 20 and 23 into Chinese. Some part were translated literally and other are by meanings. You could find the Irrlicht-1.7.1 tutorial Chinese version from here.
001shot

转载于:https://www.cnblogs.com/open-coder/archive/2013/02/06/2908227.html

内容概要:本文提出了一种基于融合鱼鹰算法和柯西变异的改进麻雀优化算法(OCSSA),用于优化变分模态分解(VMD)的参数,进而结合卷积神经网络(CNN)与双向长短期记忆网络(BiLSTM)构建OCSSA-VMD-CNN-BILSTM模型,实现对轴承故障的高【轴承故障诊断】基于融合鱼鹰和柯西变异的麻雀优化算法OCSSA-VMD-CNN-BILSTM轴承诊断研究【西储大学数据】(Matlab代码实现)精度诊断。研究采用西储大学公开的轴承故障数据集进行实验验证,通过优化VMD的模态数和惩罚因子,有效提升了信号分解的准确性与稳定性,随后利用CNN提取故障特征,BiLSTM捕捉时间序列的深层依赖关系,最终实现故障类型的智能识别。该方法在提升故障诊断精度与鲁棒性方面表现出优越性能。; 适合人群:具备一定信号处理、机器学习基础,从事机械故障诊断、智能运维、工业大数据分析等相关领域的研究生、科研人员及工程技术人员。; 使用场景及目标:①解决传统VMD参数依赖人工经验选取的问题,实现参数自适应优化;②提升复杂工况下滚动轴承早期故障的识别准确率;③为智能制造与预测性维护提供可靠的技术支持。; 阅读建议:建议读者结合Matlab代码实现过程,深入理解OCSSA优化机制、VMD信号分解流程以及CNN-BiLSTM网络架构的设计逻辑,重点关注参数优化与故障分类的联动关系,并可通过更换数据集进一步验证模型泛化能力。
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