▍1. projectplanner
说明： 3DExperience Enovia project planner
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▍2. Phase unwrapping
用于最小二乘法求解相位，求解非常实用的求解相位方法(Phase unwrapping by regions using leastsquares approach)
▍3. Phase unwrapping
说明： 用于最小二乘法求解相位，求解非常实用的求解相位方法(Phase unwrapping by regions using leastsquares approach)
▍6. Problem Solving with Excel and Matlab
Using Matlab - Problem Solving with Excel and Matlab
▍7. Problem Solving with Excel and Matlab
说明： Using Matlab - Problem Solving with Excel and Matlab
▍8. ImageGuide
OpenStack Virtual Machine Image Guide - This guide describes how to obtain, create, and modify virtual machine images that are compatible with OpenStack.
▍9. ImageGuide
说明： OpenStack Virtual Machine Image Guide - This guide describes how to obtain, create, and modify virtual machine images that are compatible with OpenStack.
▍12. wind power forecasting based on EWT-KELM
针对短期风电功率预测，提出一种基于经验小波变换预处理的核极限学习机组合预测方法。首先采用 EWT 对风电场实测风速数据进行自适应分解并提取具有傅立叶紧支撑的模态信号分量，针对每个分量分别构建 KELM 预测模型，最后对各个预测模型的输出进行叠加得到风速预测值并根据风电场风功特性曲线可得对应风电功率预测值。(Aiming at short-term wind power prediction, a kernel-based learning machine combination prediction method based on empirical wavelet transform preprocessing is proposed. Firstly, the EWT is used to adaptively decompose the measured wind speed data of the wind farm and extract the modal signal components with Fourier tight support. The KELM prediction model is constructed for each component. Finally, the output of each prediction model is superimposed to obtain the wind speed prediction value. The wind power characteristic curve of the wind farm can be obtained corresponding to the predicted wind power.)
▍13. wind power forecasting based on EWT-KELM
说明： 针对短期风电功率预测，提出一种基于经验小波变换预处理的核极限学习机组合预测方法。首先采用 EWT 对风电场实测风速数据进行自适应分解并提取具有傅立叶紧支撑的模态信号分量，针对每个分量分别构建 KELM 预测模型，最后对各个预测模型的输出进行叠加得到风速预测值并根据风电场风功特性曲线可得对应风电功率预测值。(Aiming at short-term wind power prediction, a kernel-based learning machine combination prediction method based on empirical wavelet transform preprocessing is proposed. Firstly, the EWT is used to adaptively decompose the measured wind speed data of the wind farm and extract the modal signal components with Fourier tight support. The KELM prediction model is constructed for each component. Finally, the output of each prediction model is superimposed to obtain the wind speed prediction value. The wind power characteristic curve of the wind farm can be obtained corresponding to the predicted wind power.)
▍14. micro-grid based on CSO
本文分析微网中微电源包括光伏发电、风力发电、微燃机、柴油发电机和燃料电池的电气特性，构建微电网优化运行的模型，以微网的经济成本和环境成本最小为目标函数，充分考虑了电压越限、功率平衡、微电源出力限制等约束条件，应用鸡群算法进行求解。 解决了粒子群算法易早熟、易陷入局部最优解的问题。并通过典型的微网系统进行仿真分析，仿真结果验证了该算法的有效性。(In this paper, the electrical characteristics of micro-power sources in micro-grid, including photovoltaic power generation, wind power generation, micro-gas engine, diesel generator and fuel cell, are analyzed. The optimal operation model of micro-grid is constructed. The objective function is to minimize the economic cost and environmental cost of micro-grid. The constraints of voltage overshoot, power balance and output limitation of micro-power are fully considered. The chicken swarm algorithm is applied to solve the problem. Particle swarm optimization (PSO) is easy to premature and fall into local optimal solution. The simulation results of a typical microgrid system show the effectiveness of the proposed algorithm.)
▍15. micro-grid based on CSO
说明： 本文分析微网中微电源包括光伏发电、风力发电、微燃机、柴油发电机和燃料电池的电气特性，构建微电网优化运行的模型，以微网的经济成本和环境成本最小为目标函数，充分考虑了电压越限、功率平衡、微电源出力限制等约束条件，应用鸡群算法进行求解。 解决了粒子群算法易早熟、易陷入局部最优解的问题。并通过典型的微网系统进行仿真分析，仿真结果验证了该算法的有效性。(In this paper, the electrical characteristics of micro-power sources in micro-grid, including photovoltaic power generation, wind power generation, micro-gas engine, diesel generator and fuel cell, are analyzed. The optimal operation model of micro-grid is constructed. The objective function is to minimize the economic cost and environmental cost of micro-grid. The constraints of voltage overshoot, power balance and output limitation of micro-power are fully considered. The chicken swarm algorithm is applied to solve the problem. Particle swarm optimization (PSO) is easy to premature and fall into local optimal solution. The simulation results of a typical microgrid system show the effectiveness of the proposed algorithm.)
▍16. optimized_datacenter_cloud
Towards an optimized abstracted topology design in cloud environment
▍17. optimized_datacenter_cloud
说明： Towards an optimized abstracted topology design in cloud environment
▍18. 实用负载均衡技术：网站性能优化攻略
这是一本关于网站架构、负载均衡、性能优化方面的书籍(Books on Web Architecture, Load Balancing, Performance Optimization)
▍19. 实用负载均衡技术：网站性能优化攻略
说明： 这是一本关于网站架构、负载均衡、性能优化方面的书籍(Books on Web Architecture, Load Balancing, Performance Optimization)
▍20. AUTOSAR_EXP_LayeredSoftwareArchitecture
General description. Presentation
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- 优秀论文及配套源码 SVM Short-term-Load-Forecasting
- 综合能源优化含储能
- 用遗传算法改进粒子群GA-PSO算法
- 通过粒子群能够很好的解决优化问题 pso
- 天牛群算法 BSO
- 混沌粒子群优化算法
- 2019年中国研究生数学建模竞赛E题
- 智能算法的Matlab仿真程序及教程
- 《MATLAB R2016a神经网络设计与应用28个案例分析》随书代码
- BAS BP论文及代码
- denoising
- Airy 基模高斯光束
- EMS Microgrid
- 2012年全国大学生数学建模竞赛题目--葡萄酒的评价Wines-Evaluation
- GA-bp
- 本人调试的大作业 源程序模型+论文 intelligent_control_modle
- LSTM的例子、单向LSTM、双向LSTM、多层LSTM
- 遗传算法优化BP网络案例
- 通过yalmip平台调用cplex求解器 microgrid Cplex
- 基于lstm算法在MATLAB对短期风速进行预测 LSTM-regression-master
