差分进化优化灰色模型

说明：  本次实验是基于广泛测试的公开加密库来实现数字签名服务。这里选择了 M2Crypto 加密库来实现，M2Crypto 加密库能够完整的支持 OpenSSL 库，所以会 更加方便点。而且这个库中包含有 RSA 非对称加密算法（M2Crypto.RSA），高级 的加密解密接口（M2Crypto.EVP）,还能操作 X.509 证书(M2Crypto.X509)。我们 可以先通过 M2Crypto 库里的模块来生成证书、公钥文件和私钥文件，后面再利 用生成的证书、私钥和公钥来实现加密解密及数字签名认证。(This experiment is based on a widely tested public encryption library to implement digital signature services. Here is the choice The M2Crypto encryption library is implemented, and the M2Crypto encryption library can fully support the OpenSSL library, so it will It's more convenient. And this library contains a RSA asymmetric encryption algorithm (M2Crypto.RSA), high level The encryption and decryption interface (M2Crypto.EVP) can also operate the X.509 Certificate (M2Crypto.X509). We A certificate, a public key file, and a private key file can be generated by the modules of the M2Crypto repository first, and the later will be beneficial. The encrypted decryption and digital signature authentication are realized with the generated certificate, private key and public key.)

osvos视频分割论文代码

可以把论文从英文翻译到中文，方便论文的阅读(Translate a English paper to Chinese.)

说明：  基于深度学习的OFDM多载波系统信道估计(Channel estimation for OFDM multi carrier system based on deep learning)

通过Python给Shape文件添加字段，需要ArcGIS 10.0 以上版本支持

利用Python实现卷积神经网络，适合初学者借鉴，参考。(Using Python to realize convolution neural network is suitable for beginners to learn and refer to.)

非常好的蒙特卡洛树搜索写的五子棋示例，核心代码都有中文说明(An Example of Gobang Written by Monte Carlo Tree Search)

主要用Python的pygame库实现微信打飞机小游戏，实现了基本功能，包括游戏难度的升级，飞机数量的控制，子弹速度的控制

kaggle-浮游生物分类比赛一等奖（采用深度学习算法） -译文，主要包括： 1）介绍 2）预处理和data augmentation 3）network 结构 4）模型训练 5）非监督和半监督方法 6）model averaging 7）汇总（Miscellany） 8）总结(kaggle- plankton classification competition first prize (deep learning algorithm) - translation, including: 1) Introduction 2) pre-processing and data augmentation 3) network structure 4) model training 5) unsupervised and semi-supervised method 6 ) model averaging 7) Summary (Miscellany) 8) summarized)

爬取微博内容，通过python进行抓取分析(Microblogging information crawling layer is responsible for climbing the microblogging information and downloading the file shared by microblogging user.)