-
基于时间窗的电动车调度 VRPTW-master
基于时间窗的电动车调度，利用了遗传算法，启发式算法进行编程(Electric vehicle scheduling based on time window is programmed by using genetic algorithm and heuristic algorithm.)
- 2020-06-25 00:40:02下载
- 积分：1
-
learn
说明： 解非线性方程组，使用python语言，两种方法解决非线性方程组，对比分析性能(Solving the non-linear equations, using Python language, two methods to solve the non-linear equations, comparative analysis of performance)
- 2020-06-23 21:00:01下载
- 积分：1
-
行列式转置(reverseArray.py)
行列式的转置
- 2020-12-09下载
- 积分：1
-
该资源是关于Python软件的安装环境参考教学文档
说明： 该资源是关于Python软件的安装环境参考教学文档的(The resource is about the installation environment of the Python software refer to the teaching document.)
- 2020-06-25 00:40:02下载
- 积分：1
-
emd_example
emd example for python
- 2020-06-16 12:40:02下载
- 积分：1
-
禁忌搜索算法调度例子
说明： 用禁忌搜索算法求解一个简单的调度任务，编程语言为python(Tabu search algorithm is used to solve a simple scheduling task. The programming language is python)
- 2020-04-13 19:30:02下载
- 积分：1
-
chedaoxian
说明： 利用计算机视觉，在python语言下，实时对车道线进行识别检测(direct and show the lane from video)
- 2020-12-31 14:22:01下载
- 积分：1
-
可以
python3.6实现的一些信号处理过程。(Some signal processing procedures implemented by python3.6.)
- 2017-08-22 10:56:07下载
- 积分：1
-
自动提取应力结果保存
说明： 采用Python写的abaqus自动提取应力结果的命令(A command written by Python to automatically extract stress results from ABAQUS)
- 2021-03-03 11:59:33下载
- 积分：1
-
joint_sparse_algorithms-master
说明： 我们描述了所提出的方法对超声（US）信号的压缩多路复用的直接应用。该技术利用压缩多路复用器架构进行信号压缩，并依靠频域中US信号的联合稀疏性进行信号重建。由于换能器元件具有压电特性，因此可以获得有关US信号频率支持的准确先验知识，并且可以在联合稀疏算法中使用。
我们在数值实验中验证了所提出的方法，并显示了它们在秩次缺陷情况下相对于最新方法的优越性。我们还证明，与没有已知支持的重建相比，该技术可显着提高体内颈动脉图像的图像质量。(We describe a direct application of the proposed methods for compressive multiplexing of ultrasound (US) signals. The technique exploits the compressive multiplexer architecture for signal compression and relies on joint-sparsity of US signals in the frequency domain for signal reconstruction. Due to piezo-electric properties of transducer elements, accurate prior knowledge of the frequency support of US signals is available and can be used in joint-sparse algorithms.
We validate the proposed methods on numerical experiments and show their superiority against state-of-the-art approaches in rank-defective cases. We also demonstrate that the techniques lead to a significant increase of the image quality on in vivo carotid images compared to reconstruction without known support.)
- 2020-03-16 16:45:38下载
- 积分：1
