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oracle的内部培训资料，可能有些老，但还是很实用-oracle

SAR processing library

说明：  形成策略被用来形成稳定的生态位，小生境/子种群被优化以并行地搜索和维护帕累托最优解。此外，还提出了一种自组织机制，以提高物种制定的效率和算法的性能。为了在决策空间和目标空间保持解决方案的多样性，SS-MOPSO采用了非主导排序方案和特殊拥挤距离技术。(In the proposed method, the speciation strategy is used to form stable niches and these niches/subpopulations are optimized to search and maintain Pareto-optimal solutions in parallel. Moreover, a self-organized mechanism is proposed to improve the efficiency of the species formulation as well as the performance of the algorithm. To maintain the diversity of the solutions in both the decision and objective spaces, SS-MOPSO is incorporated with the non-dominated sorting scheme and special crowding distance techniques. The performance of SS-MOPSO is compared with a number of the state-of-the-art multi-objective optimization algorithms on fourteen test problems. Moreover, the proposed SS-MOSPO is also employed to solve a real-life problem.)

微人事是一个前后端分离的人力资源管理系统，项目采用SpringBoot+Vue开发(Micro Personnel is a front-end and back-end separated human resources management system. The project is developed by SpringBoot+Vue.)

通过认证，未通过认证，删除，生成文件等简单功能 后台管理：look.asp 默认密码：admin-through certification, not through certification, delete, generate simple functional document management background : look.asp Default Password : admin

说明：  基于最大类间方差阈值与遗传算法的图像分割及MATLAB仿真分析(Image segmentation based on maximum inter class variance threshold and genetic algorithm and MATLAB simulation analysis)

肖特基二极管使用说明,很详细的,一定好用(Schottky diodes for use, very detailed, must use)

说明：  独立成分分析(Independent Component Analysis，ICA)是近年来提出的非常有效的数据分析工具，它主要用来从混合数据中提取出原始的独立信号。它作为信号分离的一种有效方法而受到广泛的关注。近几年出现了一种快速ICA算法(Fast ICA)，该算法是基于定点递推算法得到的，它对任何类型的数据都适用，同时它的存在对运用ICA分析高维的数据成为可能。又称固定点(Fixed-Point)算法，是由芬兰赫尔辛基大学Hyvärinen等人提出来的。是一种快速寻优迭代算法，与普通的神经网络算法不同的是这种算法采用了批处理的方式，即在每一步迭代中有大量的样本数据参与运算。但是从分布式并行处理的观点看该算法仍可称之为是一种神经网络算法。(Independent component analysis (ICA) is a very effective data analysis tool proposed in recent years. It is mainly used to extract the original independent signals from the mixed data. As an effective method of signal separation, it has been widely concerned. In recent years, a fast ICA algorithm (fast ICA) has appeared. The algorithm is based on the fixed-point recursive algorithm, which is applicable to any type of data. At the same time, its existence makes it possible to use ICA to analyze high-dimensional data. Also known as fixed-point algorithm, it was proposed by HYV & auml; rinen et al. It is a fast optimization iterative algorithm. Different from the common neural network algorithm, this algorithm adopts the way of batch processing, that is, there are a large number of sample data in each iteration. But from the point of view of distributed parallel processing, this algorithm can still be called a neural network algorithm.)

LS dyna 二次开发所用的学习资料。Fortran 语言(the fortran used for user define in LS dyna)

多线程lock版生产者模式和消费者模式，爬虫(Multi-threaded lock version producer mode and consumer mode, crawler)