matlab潮流计算含代码线路中断分布因子-LODF基于用户给定的分支中断数目-功率流- 项目博客文章链接在这里： 该项目需要使用快速解耦的XB版本根据用户给定的分支中断数来计算线路中断分布因子（LODF）。 LODF值用于基于偶发事件之前的事前分支流量和基本分支流量来估计事后事件分支流量。 将MW和MVA流量的AC功率流与DC方法进行比较。 在同一WinZip文件中提供了源代码，用于计算LODF值和近似的偶发事件后分支流。 结论： 我通过编写代码来确定摆动总线来开始程序流程，因为它在计算线路中断分布因子并简化B素数矩阵方面起着重要的作用。 主程序打印出每个分支号的清单，从总线到总线，并要求用户输入分支中断号。 该程序将仅测试4、5、6、7、8或9上的分支中断，并循环搜索更多请求，直到用户输入可接受的数字为止。 在为分支中断选择了选定的分支编号之后，将wcc9bus分支数据复制到新矩阵，并删除分支中断的相应行。 然后从缺少分支中断行的新分支矩阵形成B-素数矩阵。 然后，主程序调用LODF函数。 给定分支中断，wcc9bus分支数据和B-素数矩阵，LODF函数（computeLODF.m

我们研究了在不同的反向偏置值下，pin型和独立吸收与倍增（SAM）型GaN雪崩光电二极管中电场的分布。 我们还分析了每一层参数（包括宽度和浓度）对电场分布（尤其是击穿电压）的影响。 发现较高的p-GaN浓度（高于1x10（18）cm（-3））和较低的i-GaN载流子浓度（低于5x10（16）cm（-3））有助于限制电场并降低击穿电压。 在SAM（PININ）结构，合适的选择应为浓度与中间的n-GaN层的厚度，以恶化为销结构降低击穿电压和防止设备制成。 最后，提出了各层材料的优化参数。

Power Distribution Networks with On-Chip Decoupling Capacitors, 2nd edition is dedicated to distributing power in high speed, high complexity integrated circuits with power levels exceeding many tens of watts and power supplies below a volt. This book provides a broad and cohesive treatment of power distribution systems and related design problems, including both circuit network models and design techniques for on-chip decoupling capacitors. The book provides insight and intuition into the behavior and design of on-chip power distribution systems. This book has four primary objectives. The first objective is to describe the impedance characteristics of the overall power distribution system, from the voltage regulator through the printed circuit board and package onto the integrated circuit to the terminals of the on-chip circuitry. The second objective is to discuss the inductive characteristics of on-chip power distribution grids and the related circuit behavior of these structures. The third objective is to present design methodologies for efficiently placing on-chip decoupling capacitors in nanoscale integrated circuits. Finally, the fourth objective is to suggest novel architectures for distributing power across an integrated circuit, as well as provide new methodologies to efficiently analyze and design on-chip power grids. Organized into subareas to provide a more intuitive flow to the reader, this edition adds more than a hundred pages of new content, including inductance models for interdigitated structures, design strategies for multi-layer power grids, advanced methods for efficient power grid design and analysis, and methodologies for simultaneously placing on-chip multiple power supplies and decoupling capacitors. The emphasis of this additional material is on managing the complexity of on-chip power distribution networks.

本机环境： cudatoolkit = 10.1.243 cudnn = 7.6.5 tensorflow-gpu = 2.1.0 keras-gpu = 2.3.1 今天在以TensorFlow2.1.0为后端的Keras中使用TensorBoard时报错，发现原因是keras和tf.keras混用导致的。报错与解决方案如下： 导致报错语句： summary = TensorBoard(log_dir="cnn_lstm_logs/",histogram_freq=1) 报错： ---> 54 summary = TensorBoard(log_dir="cnn_lstm_log

一、累积分布函数(Cumulative Distribution Function) 累积分布函数(Cumulative Distribution Function)，又叫分布函数，是概率密度函数的积分，能完整描述一个实随机变量X的概率分布。一般以大写CDF标记,与概率密度函数probability density function（小写pdf）相对。 累计分布函数的特性： ①因为累计分布函数是计算x点左侧的点的数量，所以累计分布函数CDF是单调递增的。 ②CDF比没有直方图变化剧烈，但是CDF包含了相同的信息，并且减少了噪声。 ③由于CDF不存在装箱（分段），因此比直方图能更好的展现数据。

This specification describes two levels of interfaces: • A lower DCPS (Data-Centric Publish-Subscribe) level that is targeted towards the efficient delivery of the proper information to the proper recipients. • An optional higher DLRL (Data Local Reconstruction Layer) level, which allows for a simple integration of the Service into the application layer.

The material in this document details an Object Management Group specification in accordance with the terms, conditions and notices set forth below. This document does not represent a commitment to implement any portion of this specification in any company's products. The information contained in this document is subject to change without notice.

Data Distribution Service for Real-time Systems Version 1.2 OMG Available Specification formal/09-01-01

本代码是用于对给定（非平稳）信号进行离散 Wigner-Ville 时频分析的 Matlab 程序。 % 离散 Wigner-Ville 分布% x = 输入信号% fs = 采样率% K = 内核值% DVW = DWVT %这个程序是免费软件； 您可以重新分发和/或修改

深度学习中的分布外 (OoD) 泛化是指模型在分布变化的场景下进行泛化的任务。现在受到众多关注。最近，清华大学崔鹏等研究者发布了《分布外泛化(Out-Of-Distribution Generalization)》综述论文，针对该领域的系统、全面地探讨了OOD泛化问题，从定义、方法、评价到启示和未来发展方向。