让自己开心一刻钟,请看【第一次坐飞机】的笑话O(∩_∩)O

                                    First Flight】 

Mr. Johnson had never been up in an aerophane before and he had read a lot about air accidents,

 

so one day when a friend offered to take him for a ride in his own small phane,

 

Mr. Johnson was very worried about accepting.

 

 Finally, however,

 

 his friend persuaded him that it was very safe,

 

and Mr. Johnson boarded the plane.

 

His friend started the engine and began to taxi onto the runway of the airport. Mr.

 

Johnson had heard that the most dangerous part of a flight were the take-off and the landing,

 

 so he was extremely frightened and closed his eyes.

 

After a minute or two he opened them again,

 

looked out of the window of the plane,

 

and said to his friend, "Look at those people down there. They look as small as ants, don't they?"

 

"Those are ants," answered his friend. "We're still on the ground."

                            

                                                  第一次坐飞机


  约翰逊先生从前未乘过飞机,他读过许多关于飞行事故的报道。所以,有一天一位朋友邀请他乘自己的小飞机飞行时,约翰逊先生非常担心,不敢接受。不过,由于朋友不断保证说飞行是很安全的,约翰逊先生终于被说服了,登上了飞机。

  他的朋友启动引擎开始在机场跑道上滑行。约翰逊先生听说飞行中最危险的是起飞与降落,所以他吓得紧闭双眼。

  过了一两分钟,他睁开双眼朝窗外望去,接着对朋友说道:“看下面那些人,他们看起来就象蚂蚁一样小,是不是?”

  “那些就是蚂蚁,”他的朋友答道,“我们还在地面上。”


 
 

 

【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)内容概要:本文提出了一种基于融合鱼鹰和柯西变异的麻雀优化算法(OCSSA)优化变分模态分解(VMD)参数,并结合卷积神经网络(CNN)与双向长短期记忆网络(BiLSTM)的轴承故障诊断模型。该方法利用西储大学公开的轴承数据集进行验证,通过OCSSA算法优化VMD的分解层数K和惩罚因子α,有效提升信号分解精度,抑制模态混叠;随后利用CNN提取故障特征的空间信息,BiLSTM捕捉时间序列的动态特征,最终实现高精度的轴承故障分类。整个诊断流程充分结合了信号预处理、智能优化与深度学习的优势,显著提升了复杂工况下轴承故障诊断的准确性与鲁棒性。; 适合群:具备一定信号处理、机器学习及MATLAB编程基础的研究生、科研员及从事工业设备故障诊断的工程技术员。; 使用场景及目标:①应用于旋转机械设备的智能运维与故障预警系统;②为轴承等关键部件的早期故障识别提供高精度诊断方案;③推动智能优化算法与深度学习在工业信号处理领域的融合研究。; 阅读建议:建议读者结合MATLAB代码实现,深入理解OCSSA优化机制、VMD参数选择策略以及CNN-BiLSTM网络结构的设计逻辑,通过复现实验掌握完整诊断流程,并可进一步尝试迁移至其他设备的故障诊断任务中进行验证与优化。
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