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Together_CZ的留言板
墨衍会员 · AI 创作全网分发 墨衍智能分发 通过墨衍一键同步至各平台 可同步平台 CSDN微信公众号微博知乎掘金百家号博客园抖音小红书 你的文章也可以这样分发 墨衍会员 ¥399起/年，写一次发全网
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坦克大战tank.zip
坦克大战tank：很古老却又经典的一款小游戏，完全基于python开发，我将其打包生成exe文件，感兴趣的可以拿去玩哈。 相应的博客介绍在这里：https://blog.csdn.net/Together_CZ
i-vector的工具箱
MSR Identity Toolbox: A Matlab Toolbox for Speaker Recognition Research Version 1.0 Seyed Omid Sadjadi, Malcolm Slaney, and Larry Heck Microsoft Research, Conversational Systems Research Center (CSRC) s.omid.sadjadi@gmail.com, {mslaney,larry.heck}@microsoft.com This report serves as a user manual for the tools available in the Microsoft Research (MSR) Identity Toolbox. This toolbox contains a collection of Matlab tools and routines that can be used for research and development in speaker recognition. It provides researchers with a test bed for developing new front-end and back-end techniques, allowing replicable evaluation of new advancements. It will also help newcomers in the field by lowering the “barrier to entry”, enabling them to quickly build baseline systems for their experiments. Although the focus of this toolbox is on speaker recognition, it can also be used for other speech related applications such as language, dialect and accent identification. In recent years, the design of robust and effective speaker recognition algorithms has attracted significant research effort from academic and commercial institutions. Speaker recognition has evolved substantially over the past 40 years; from discrete vector quantization (VQ) based systems to adapted Gaussian mixture model (GMM) solutions, and more recently to factor analysis based Eigenvoice (i-vector) frameworks. The Identity Toolbox provides tools that implement both the conventional GMM-UBM and state-of-the-art i-vector based speaker recognition strategies. A speaker recognition system includes two primary components: a front-end and a back-end. The front-end transforms acoustic waveforms into more compact and less redundant representations called acoustic features. Cepstral features are most often used for speaker recognition. It is practical to only retain the high signal-to-noise ratio (SNR) regions of the waveform, therefore there is also a need for a speech activity detector (SAD) in the fr
mongodb-linux-x86_64-ubuntu2004-8.0.13.tgz
mongodb-linux-x86_64-ubuntu2004-8.0.13
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三相四桥臂电压不对称跌落补偿动态电压恢复器DVR（Simulink仿真实现）
内容概要：本文围绕三相四桥臂动态电压恢复器（DVR）在电压不对称跌落情况下的补偿能力展开，重点介绍了基于Simulink平台构建的仿真模型，用于研究和验证DVR对电网电压不平衡的动态补偿性能。系统采用三相四桥臂逆变器结构，利用其独立的中性线控制能力有效处理零序分量，结合多准PR（Proportional Resonant）控制策略实现对正负序电压分量的精确跟踪与快速响应，从而提升在不平衡负载或电网故障条件下DVR的补偿精度与系统稳定性。文中提供了完整的仿真模型及相关参考文献，涵盖控制算法设计、系统建模与参数整定等关键技术环节，适用于复杂电能质量问题的研究与教学演示。; 适合人群：电气工程、电力电子与电力系统相关专业的研究生、科研人员及从事电能质量治理的技术工程师；具备一定的电路理论、自动控制原理和Simulink仿真基础者更佳。; 使用场景及目标：①研究动态电压恢复器在三相电压不对称跌落工况下的补偿机制与控制策略；②掌握三相四桥臂逆变器在不平衡条件下的建模与控制方法；③深入理解多准PR控制器在正负序分离控制中的应用，提升对电能质量调节装置的设计与仿真能力； 阅读建议：建议结合提供的参考文献与说明文档，逐步搭建并调试Simulink仿真模型，重点关注控制器参数设计与系统动态响应特性，通过设置不同类型的电压跌落场景（如单相跌落、两相短路等）来验证控制策略的有效性与鲁棒性。
