条形码识别matlab程序

代码格式化 keil外挂工具 格式化软件(Formatter Keil plug-in tool Formatting Software)

压缩包里的内容是关于buck逆变器模型搭建(On the buck inverter model built)

C#小程序仅供参考，学习使用 ，内容丰富(C# small program for reference only, learning and use, rich in content)

星云晒米正在编译易模块"精易模块", 文件名: "C:UsersxiaoqianDesktop精易模块v7.5[模块]精易模块[v7.5].ec", 版本号: "7.5", 创建号: "0.0", 作者: "广大易友". 正在进行名称连接... 正在统计需要编译的子程序 正在编译... 正在生成主程序入口代码 程序代码编译成功 等待用户输入欲编译到的文件名 正在进行名称连接... 开始静态链接... 正在写出可执行文件(Nebula Sun Rice is compiling the Easy Module, the file name: "C: Users xiaoqian Desktop Easy Module v7.5 [Module] Easy Module [v7.5]. ec", version number: "7.5", creation number: "0.0", author: "Easy Friends". Name connection is in progress. Statistics of subprograms that need to be compiled Is compiling ____________. Generating main program entry code Successful compilation of program code Waiting for the user to enter the file name to be compiled Name connection is in progress. Start static links... Writing an executable file)

Projects with an arduino to starting learning

说明：  提取图片的HOG特征，特征向量为9维，向量存于TXT文件中。(Extract the HOG feature of an image, and the feature vector is 9 dimensions, which is stored in TXT file.)

说明：  使用ga算法改进elman神经网络。优化elman参数初始值(Using GA algorithm to improve Elman neural network)

串口源码，代码附带说明很详细，新手可参考练习(Serial source code, code with detailed instructions, novice can refer to the exercise)

这是我费尽苦心所得，它用了大量的电子学知识和大量文化，可以了吧-This is my painstaking proceeds, it takes a lot of electronics and a large number of cultural knowledge, it can

说明：  针对使用传统分类器预测配变重过载会因为重过载样本率较低而带来的总正确率很高，重过载预测正确率却很低这一问题，将重抽样与随机森林理论引入分类模型中，构建重抽样-随机森林分类器对配变重过载进行预测。结果表明，新方法在预测配变日重过载类型、重过载开始与结束时间、重过载严重程度方面有较高的准确率。(For the problem of using the traditional classifier to predict the distribution of heavy overload, the total correct rate is high because of the low overload sample rate, and the accuracy of the heavy overload prediction is very low. The resampling and random forest theory are introduced into the classification model. In the construction, the re-sampling-random forest classifier is used to predict the heavy-duty overload. The results show that the new method has higher accuracy in predicting the type of daily variable overload, the start and end time of heavy overload, and the severity of heavy overload.)