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爬取招投标信息和行业规范信息，采用scrapy来分别实现 MongoDB 存储、 MySQL 存储 、 文件存储的 多个 Pipeline。

一个python写的验证码识别程序，依赖PIL、Tesseract-OCR,包括简单的噪点去除等等，识别率还不错

说明：  Openlayer geoserver 中Layer View 必备的(when you use geoserver to show your layer map ,you have to get openlayer.js include ol.js and ol.css to finish your own function to view a map.This is a sample which use geoserver and openlayer)

说明：  刀具标准数据集一部分，主要用于机械类，进行故障诊断与预测。官网也存在。(PHM10 dataset, the main task is to realize the function for fault prediction and analysis.)

说明：  关于深度学习的一些论文资料，大家可以借鉴看一下(some pdf file about deep learning , thesises , we can study it for deep learing)

图片转化成g代码，使得能够识别出图片上的内容以便画出来(The image is converted into g code, making it possible to recognize the contents of the picture for drawing)

训练了一个CNN模型用于土地资源分类，适用于遥感图像(A CNN model is trained for land use classification and is suitable for remote sensing images.)

用thensorflow实现对VGGNET深度卷积神经网络的建立。(The establishment of the VGGNET convolutional neural network is implemented with the thenthraceflow.)

说明：  bugid for automatic crashes classification

说明：  UNet最早发表在2015的MICCAI上，短短3年，引用量目前已经达到了4070，足以见得其影响力。而后成为大多做医疗影像语义分割任务的baseline，也启发了大量研究者去思考U型语义分割网络。而如今在自然影像理解方面，也有越来越多的语义分割和目标检测SOTA模型开始关注和使用U型结构，比如语义分割Discriminative Feature Network(DFN)(CVPR2018)，目标检测Feature Pyramid Networks for Object Detection(FPN)(CVPR 2017)等。(Its influence has reached 70% in 2015. Then it became the baseline that most of the medical image semantic segmentation tasks, and inspired a large number of researchers to think about the U-shaped semantic segmentation network. In the aspect of natural image understanding, more and more SOTA models of semantic segmentation and object detection begin to pay attention to and use U-shaped structure, such as semantic segmentation, discriminative feature network (DFN) (cvpr2018), feature pyramid networks for object detection (FPN) (CVPR 2017), etc.)