heu592893792

关于matlab中文件打开关闭读取等操作的命令解释(Open the file on matlab operations such as close reading command interpreter)

Image color model conversion from any image format to RGB, and vice versa.

classification d image avec l algo FCM

单边放炮数值模拟程序，对于初学地震勘查的同行是很有用的模板。(csp modelling and the modelling of seismic in geophysics)

模拟退火算法matlab程序，有详细使用说明。(Simulated Annealing Matlab codes)

线性回归补偿模型，用于转炉炼钢重点预测模型(Linear regression compensation model)

基于MATLAB利用Particle-in-cell模型,对梯形管内壁等离子体离子注入过程,进行了二维数值模拟.计算结果表明在中心电极附近出现了“阳极鞘层”,该鞘层内部不存在离子,而且在鞘层边缘离子密度最高.在上下管壁上的离子注入剂量呈现“m”形分布.通过对注入过程中等离子体密度分布和不同时间段管壁不同位置离子注入剂量的跟踪,发现“阳极鞘层”扩展行为是导致“m”形分布的原因.由于梯形管形状的不对称性,“阳极鞘层”的边缘向梯形长底方向扩展较快.在注入初始时刻离子注入的能量很低,随着时间延长离子能量逐渐升高,这是由离子初始位置决定的.可见梯形管自身形状决定了鞘层形状和最终的离子注入能量和剂量分布.(Based on MATLAB use Particle-in-cell model, the trapezoidal pipe wall plasma ion implantation process, the numerical simulation of two-dimensional calculation results show that in the vicinity of the center electrode to a "anode sheath," which does not exist inside the plasma sheath, and the highest in the sheath edge ion density ion implantation dose showing the upper and lower walls of the "m"-shaped distribution through the process of injecting the plasma density distribution and the wall at different locations in different time periods ion implantation dose tracking, found that "anode sheath "extension behavior is leading to" m "-shaped distribution of the reasons due to the asymmetry of the trapezoidal shape of the tube," Edge anode sheath "extension to the end of a long trapezoid direction quickly in the initial moment of implantation energy ion implantation is low ion energy with time gradually increased, which is determined by the initial position of the ion. visible trapezoida)

深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)

VB程序中实现调用MATLAB的方法. 介绍了在VB 应用程序中利用动态数据交换和ActiveX自动化(OLE自动化)协议实现的两种调用MATLAB函数的方法。通过这两种方法实现了VB的可视化界面与MATLAB 强大的数值分析和图形显示的能力的结合。(Introduced in VB application using Dynamic Data Exchange and ActiveX Automation (OLE Automation) protocol calls MATLAB function of the two methods. Achieved by either method, the visual interface of VB and MATLAB powerful numerical analysis and graphical display of the ability of combination.)

Based on the rough set theory and its reduction methods, a MATLAB interface is written and included in the RSDA Toolbox