论文1（附完整GAMS程序，在附录中）

说明：  移动型3-UPU并联机构运动/力传递性能(Motion / force transfer performance of 3-UPU parallel mechanism)

k-means算法实现WEB搜索，用模拟退火，对K-means聚类算法进行数据挖掘(k-means algorithm WEB search)

说明：  锂电池BMS开发源码，BMS功能开发，SOC计算，锂电池保护等(BMS development source of lithium battery)

两路ADC+DMA,stm32f103,双通道采集(ADC+DMA,stm32f103,2adc)

分析了永磁同步电机矢量控制的发展概况，然后从机电能量转换的角度出发，解释三相永磁同步电机的机电能量转换原理，推导拉格朗日运动方程。此外，列写出永磁同步电机在三相静止坐标系和dq坐标系下的数学模型。基于Simulink建立了转速电流双闭环矢量控制系统的仿真模型，通过对仿真结果分析，验证了永磁同步电机矢量控制系统性能的优越性(The development of vector control of permanent magnet synchronous motor is analyzed. Then, from the perspective of electromechanical energy conversion, the principle of electromechanical energy conversion of three-phase permanent magnet synchronous motor is explained, and the Lagrangian motion equation is derived. In addition, the mathematical model of the permanent magnet synchronous motor in the three-phase stationary coordinate system and the dq coordinate system is written. Based on Simulink, the simulation model of the speed and current double closed-loop vector control system is established. The simulation results are analyzed to verify the superiority of the performance of the permanent magnet synchronous motor vector control system.)

说明：  可实现基本循迹4轮，4传感器，红外，自由旋转等功能(It can realize the functions of basic tracing 4 wheels, 4 sensors, infrared, free rotation and so on.)

线性核支持向量机，径向基支持向量机，多项式支持向量机(Linear kernel support vector machine, radial basis support vector machine, polynomial support vector machine)

ida调试so简单教程, ida调试so简单教程,(Ida debugging so simple tutorial)

如果你满足以下条件： ? 会使用电脑，但从来没写过程序； ? 还记得初中数学学的方程式和一点点代数知识； ? 想从编程小白变成专业的软件架构师； ? 每天能抽出半个小时学习。 那么你就能学会python(If you meet the following criteria: Can use a computer, but never write a program; Remember junior high school math equations and a little algebra; Want to go from programming whiz to professional software architect; I can spare half an hour to study every day. Then you can learn python)

说明：  这些文件将在两用户非正交多址（NOMA）系统中实现用于信号检测的深度学习方法[1]。三个主要脚本分别是生成训练数据、训练神经网络和生成测试结果。该神经网络用于相位衰落的静态标量信道，用于NOMA系统中同时检测2个用户的单个子载波上的传输符号。考虑并测试了两种情况：一种是导频符号数目较少，另一种是循环前缀长度较短。在这两种情况下，深度学习方法都比传统的信道估计方法具有更强的鲁棒性。(These files are to implement the deep learning method for signal detection in a two-user non-orthogonal multiple access (NOMA) system [1]. The 3 main scripts are to generate training data, to train the neural network and to produce testing results, respectively. The neural network is trained for a static scalar channel with phase fading and is used to detect transmitted symbols on a single subcarrier for 2 users simultaneously in a NOMA system. Two scenarios are considered and tested: one is with fewer number of pilot symbols and the other is with shorter length of cyclic prefix. The deep learning method is shown to be more robust than conventional channel estimation methods in both cases.)