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chemkin
说明： 重油燃烧机理chemkin机理文件，反应步数较多，需要较大服务器计算(Heavy oil combustion mechanism chemkin mechanism file, more reaction steps.)
- 2021-04-07 12:39:01下载
- 积分：1
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distillation
说明： 使用PYTHON对精馏过程进行编程，比较实用(Using Python to program distillation process is more practical)
- 2021-03-15 19:59:22下载
- 积分：1
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JCore
说明： 对abaqus有限元程序进行二次开发的J积分运算(J-integral operation of ABAQUS finite element program)
- 2021-04-08 17:09:00下载
- 积分：1
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知乎爬虫
说明： 简单的知乎爬虫，输入关键词，爬取该关键词下的提问，回答，点赞数等，以csv格式存储(Simple Zhihu crawler, input key words, crawl the questions, answers and likes under the key words, and store them in CSV format)
- 2020-04-13 12:01:26下载
- 积分：1
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Python-opencv车牌识别
算法思想来自于网上资源，先使用图像边缘和车牌颜色定位车牌，再识别字符。车牌定位在predict方法中，为说明清楚，完成代码和测试后，加了很多注释，请参看源码。车牌字符识别也在predict方法中，请参看源码中的注释，需要说明的是，车牌字符识别使用的算法是opencv的SVM， opencv的SVM使用代码来自于opencv附带的sample，StatModel类和SVM类都是sample中的代码。SVM训练使用的训练样本来自于github上的EasyPR的c++版本。由于训练样本有限，你测试时会发现，车牌字符识别，可能存在误差，尤其是第一个中文字符出现的误差概率较大。(Bits in the predict method, for clarity, add a lot of comments after completing the code and testing, see the source code. License plate character recognition is also included in the predict method. Please refer to the comments in the source code. It needs to be noted that the algorithm used for license plate character recognition is SVM of opencv. The training sample used for SVM training comes from the c++ version of EasyPR on github. Due to limited training samples, you may find errors in license plate character recognition when testing, especially the error probability of the first Chinese character is large.)
- 2018-08-30 16:46:33下载
- 积分：1
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LSTM入门正弦波序列预测
LSTM入门学习，正弦波序列预测
for i, input_t in enumerate(input.chunk(input.size(1), dim=1)):
h_t, c_t = self.lstm1(input_t, (h_t, c_t))
h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
output = self.linear(h_t2) # output.shape:[batch,1]
outputs = [output] # outputs.shape:[[batch,1],...[batch,1]], list composed of n [batch,1],
for i in range(future): # if we should predict the future
h_t, c_t = self.lstm1(output, (h_t, c_t))
h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
output = self.linear(h_t2) # output.shape:[batch,1]
outputs = [output] # outputs.shape:[[batch,1],...[batch,1]], list composed of n [batch,1],
outputs = torch.stack(outputs, 1).squeeze(2) # shape after stack:[batch, n, 1], shape after squeeze: [batch,n]
return outputs
- 2021-06-30 00:31:01下载
- 积分：1
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GAT-master
图注意力机制神经网络，添加监督信息来训练图神经网络(Training Graph Neural Network for Classification)
- 2021-05-13 00:30:02下载
- 积分：1
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GAT-master
说明： 图注意力机制神经网络，添加监督信息来训练图神经网络(Training Graph Neural Network for Classification)
- 2021-05-13 00:30:02下载
- 积分：1
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Python/Matlab 小波包变换实例源码
1、用自己采样得到的故障诊断数据分别采用Python/MATLAB两种方式对样本数据进行故障特征提取。 2、附有数据文件夹，里面包含有9种不同的故障样本集原始数据，每一种故障样本集共有100组样本。文件夹共有900组样本数据。 3、两份用Python或MATLAB写的源码都是对故障样本进行特征提取并归一化操作，改变文件路径可以直接运行
- 2019-06-28下载
- 积分：1
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下雪
说明： 关于下雪的代码是用python实现的，用于学习交流的(The code about snow is implemented in Python for learning and communication)
- 2019-11-11 11:45:40下载
- 积分：1
