声学发射混凝土裂缝标本的深度学习：回归现场时间和失败类型的分类
如何将深度学习用于回归和分类任务？
我正面临着具有混凝土骨折的声发射的问题。目的是自动找到现场时间瞬间（在声发射开始时的时间）和具有峰值值的斜率，以确定断裂的种类（基于升高角度RA的模式I或模式II）。
我尝试使用区域CNN来使用信号更快地调整rcnn ，但不幸的是，到目前为止，结果尚不出色。
我想根据一定的采样频率与幅度数据的序列（时间序列）一起工作，但它们的长度不同。我该如何处理这个问题？
我可以根据可以在训练示例中手动标记的监督点进行1D-CNN，从而制作一种异常检测？
我有一定数量的录音，我想利用这些录音来训练100Hz采样的模型。在有关异常检测的示例中，例如 nofollow noreferrer“> limesereries”> limesereries a autoEccoder ，他们使用同一时间序列序列序列序列序列序列序列序列序列他们可以通过滑动1个时间步骤执行一个窗口，以便获得大约3700座训练其神经网络。取而代之的是，我有不同数量的录音（时间序列），每个录音（时间序列）具有一定的瞬间，并且在几秒钟内具有不同的全局长度。我该如何管理？
实际上，我需要信号开始的时间和最大点来定义升高角并分类断裂的类型。我可以与CNN直接与现场时间的回归任务同时进行分类吗？
先感谢您！
How can I use deep learning for both regression and classification tasks?
I am facing a problem with acoustic emission on fracture with concrete speciment. The objective is to find automatically the on-set time instant (time at the beginning of the acoustic emission) and the slope with the peak value to determine the kind of fracture (mode I or mode II based on the raise angle RA).
I have tried Regional CNN to work with images of the signals Fine-tuning Faster-RCNN using pytorch, but unfortunately the results are not outstanding up to now.
I would like to work with sequences (time series) of amplitude data according to a certain sampling frequency, but they have different length each. How can I deal with this problem?
Can I make a 1D-CNN which makes a sort of anomaly detection based on the supervised point that I can mark manually on training examples?
I have a certain number of recordings which I would like to exploit to train the model sampled at 100Hz. In examples on anomaly detection like Timeseries anomaly detection using an Autoencoder, they use the same time series and they perform a window with sliding 1 time step in order to obtain about 3700 to train their neural network. Instead I have different number of recordings (time series) each of them with a certain on-set time instant and different global length in seconds. How can I manage it?
I actually need the time instant of the beginning of the signal and the maximum point to define the raise angle and classify the type of fracture. Can I make classification directly with CNN simultaneously with regression tasks of the on-set time instant?
Thank you in advance!
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我终于解决了@Jonnordby的基本建议，使用了声音事件检测方法。我们从 github yashnita ）。
我根据以下图像标记数据：
然后，我采用了从计算输入信号的频谱图中提取功能的方法：
最后，我们能够对直接连接到声学发射事件检测的地震事件检测获得更精确的输出识别，从而获得以下结果：
目前，仅完成了事件识别阶段，但是还可以简单地进行易于进行裂纹模式I或模式II的分类。
I finally solved, thanks to the fundamental suggestion by @JonNordby, using Sound Event Detection method. We adopted and readapted the code from GitHub YashNita.
I labelled the data according to the following image:
Then, I adopted the method for extracting features from computing the spectrogram of the input signals:
And finally we were able to get a more precise output recognition of the Seismic Event Detection which is directly connected to the Acoustic Emission Event detection, obtaining the following result:
For the moment, only the event recognition phase was done, but it would be simple to readapt also to conduct classification of mode I or mode II of cracking.