Matlab Code for Boundary-GP Constrained Gaussian Processes with Variational Harmonic Features

此代码为高斯低通MATLAB代码，欢迎大家下载。

高斯白噪声 MATLAB 代码示例： % 生成高斯白噪声 mu = 0; % 均值 sigma = 1; % 标准差 N = 1000; % 样本数 noise = mu + sigma * randn(N, 1); % 绘制噪声信号 figure; plot(noise); title('Gaussian White Noise Signal'); xlabel('Sample Index'); ylabel('Amplitude'); 此代码用于生成和可视化高斯白噪声信号，并可以在后续的图像处理算法中应用。

Due to the increasing use of power electronic devices in power regulation, the quality of power systems has been deteriorating. As a solution, passive shunt filters are used to suppress harmonics in these systems. This is achieved through a Simulink model design and implementation, where the passive harmonic filter effectively mitigates the effects of harmonics in the power system.

此函数使用带旋转的高斯消元法求解线性系统Ax=b。该算法概述如下： 1) 初始化一个置换向量r = [1, 2,...,n]，其中r(i)对应于A中的第i行。 2) 对于k = 1,...,n-1，找到a(r(k),a(r(k+1),k),...,a(r(n),k)中最大的（绝对值）元素。 3) 假设r(j,k)是最大的元素，切换r(j)和r(k)。 4) 对于i=1,...,k-1,k+1,...,n计算：zeta = a(r(i),k) / a(r(k),k)。 5) 对于j=k,...,n，计算：a(r(i),j)=a(r(i),j)-a(r(k),j)*zeta，b(r(i)) = b(r(i))-b(r(k))*zeta。 6) 步骤1到6有效地对角化了A。 7) 解向量中的每个元素为：x(r(i)) = b(i)/a。

高斯消元法的MATLAB实现代码，提供了关于矩阵操作的优质源程序。希望大家积极下载，感谢支持！

Redis是一款开源、高性能的键值对数据库，常用于数据缓存、消息中间件以及数据库功能。Redis-7.2.4作为最新稳定版本，提供了诸多改进和新特性。以下是对Redis核心概念、7.2.4版本更新以及源码编译使用的详细介绍。 Redis核心概念1. 键值对存储：Redis的基础是键值对（key-value）存储，其中键是唯一的字符串标识，值可以是各种数据类型（如字符串、哈希、列表、集合和有序集合）。2. 数据类型：- 字符串（String）：基础类型，支持设置、获取和原子操作。- 哈希（Hash）：键值对集合，适合存储对象。- 列表（List）：双向链表，支持添加、删除元素和范围查询。- 集合（Set）：无序不重复元素集合，支持并集、交集、差集操作。- 有序集合（Sorted Set）：与集合类似，但每个元素有分数，支持按分数排序。3. 持久化：提供RDB（快照）和AOF（append-only file）两种主要持久化方式，确保服务器重启后能恢复数据。4. 事务：Redis支持多条命令的原子执行，通过MULTI和EXEC实现。5. 复制：支持主从复制，保持多个节点间数据同步，提升系统可用性。6. 集群：Redis Cluster提供自动分片和故障转移功能，实现水平扩展。 Redis-7.2.4更新1. 性能优化：7.2.4版本包含针对读写速度、内存使用等方面的优化。2. 新特性和改进：包括新的命令、API更新、更完善的错误处理机制等。3. 安全增强：包含密码认证、访问控制等方面的增强。4. 稳定性提升：修复已知bug，增强系统的稳定性和可靠性。 编译与使用源码1. 下载源码：从Redis官方网站或GitHub仓库获取Redis-7.2.4源码压缩包。2. 解压与配置：运行tar -zxvf redis-7.2.4.tar.gz解压，进入目录后，使用./configure --prefix=/your/install/path进行配置。3. 编译与安装：在终端运行make进行编译，然后运行make install完成安装。

The HowarthsTransformation.m file provides a framework for solving boundary layer problems using the Howarth's Transformation. The function takes the following parameters: y3y5_0 = HowarthsTransformation(rhofun, miufun, hw, M, Pr, Gamma, y3y5_0guess), with default values:- rhofun = @(h) h^(-1)- miufun = @(h) h^(2/3)- hw = 2- M = 0- Pr = 0.7- Gamma = 1.4- y3y5_0guess = [0.1; 2] Example 1: y3y5_0 = HowarthsTransformation() (using only the default values);Example 2: y3y5_0 = HowarthsTransformation with custom input parameters. For more details, please refer to the Wikipedia page on Howarth's transformation: https://en.wikipedia.org/wiki/Blasius_boundary_layer#Howarth_transformation

Energy Control Problem Code in MATLAB: Non-Intrusive Load Monitoring (NILM) for HVAC Systems This repository contains the dataset we collected for HVAC energy disaggregation, as well as the source code and demonstrations from our paper in IEEE Transactions on Power Systems. To the best of our knowledge, this is the first dataset collected for studying Non-Intrusive Load Monitoring (NILM) applied to Heating, Ventilation, and Air Conditioning (HVAC) systems. Energy disaggregation or Non-Intrusive Load Monitoring (NILM) addresses the problem of extracting device-level energy consumption information by monitoring the aggregated signal at a single measurement point, without the need to install meters on each individual device. This can be framed as a source separation problem where the aggregated signal is represented as a linear combination of the basic vectors in a matrix factorization framework. In this work, we utilize machine learning to predict the energy consumption pattern of each device over the course of a day. The project is part of our collaboration with [institution name]. Prerequisites: MATLAB R2015a Datasets (Temporarily unavailable. Will be available once the required permissions are granted. Apologies for the inconvenience!) Experiments We designed two different experiments to evaluate our proposed algorithm. The first experiment disaggregates the energy of the entire household into the energy consumption of all devices within the home.

This MATLAB script demonstrates how to track object features efficiently by leveraging OpenCV functions. Steps to Implement: Integrate OpenCV Functions: Ensure OpenCV is properly installed and configured with MATLAB for seamless integration. Initialize Object Tracking: Define the object or region of interest to track. Apply Feature Tracking: Use OpenCV functions like calcOpticalFlowPyrLK for feature tracking, optimizing speed and accuracy. Key Points: Ensure MATLAB supports the required OpenCV functions for smooth operation. Test the script thoroughly to ensure compatibility with specific OpenCV versions. This guide provides a step-by-step approach to effectively implement feature tracking in MATLAB using OpenCV functions.