应用软件包: ML State Monitor

APP-SW-PACK-ML-STATE-MONITOR

机器学习（ML）应用软件包ML State Monitor依赖ML的深度学习（DL）分领域，使开发人员能够在基于MCU的系统上开发和部署神经网络，构建智能传感设备。该示例软件包旨在展示如何创建风扇振动状态监测解决方案，还详细介绍了如何在嵌入式传感设备上通过不同推理引擎运行模型来验证和评估模型性能。

访问此软件包，请点击此处的下载按钮访问恩智浦MCUXpresso SDK github存储库，并遵循本应用软件包ML State Monitor实验室指南中的步骤。

软件详情

框图

ML-based System State Monitor - App SW Pack

ML-based System State Monitor - App SW Pack
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支持的器件

  • i.MX-RT1170: i.MX RT1170: 1GHz跨界MCU，配备Arm®Cortex®内核
  • K66_180: Kinetis® K66-180 MHz，双高速和全速USB，2MB闪存微控制器(MCU)，基于Arm® Cortex®-M4内核
  • LPC550x: LPC550x/S0x：基于Arm® Cortex®-M33内核基准的微控制器系列
  • LPC551X-S1X: LPC551x/S1x: 基于Arm® Cortex®-M33的入门级微控制器系列
  • LPC552x-S2x: LPC552x/S2x：基于Arm® Cortex®-M33的主流微控制器系列
  • LPC55S6x: 高效的基于Arm® Cortex®-M33的微控制器产品系列

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  • 应用示范软件

    Application Software Pack - ML State Monitor

    EXTERNAL版本 0Jan 18, 2022 16:36:00APPLICATION-SW-PACK-ML-STATE-MONITOR
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注意: 推荐在电脑端下载软件，体验更佳。

Y true 0 SSPAPP-SW-PACK-ML-STATE-MONITORzh 1 应用笔记 Application Note t789 1 zh zh zh 应用笔记 Application Note 1 1 2 English AN13562: This application note presents the process of building and deploying deep learning models for Smart Sensing Appliances. It also highlights how to validate and evaluate the performance of a model by running it through different inference engines on an Embedded Sensing Device. 1644318754124703028011 SSP 4.9 MB None None documents None 1644318754124703028011 /docs/en/application-note/AN13562.pdf 4943726 /docs/en/application-note/AN13562.pdf AN13562 documents N N 2022-02-08 Building and Benchmarking Deep Learning Models for Smart Sensing Appliances on MCUs /docs/en/application-note/AN13562.pdf /docs/en/application-note/AN13562.pdf Application Note N 645036621402383989 2024-07-17 en Sep 27, 2023 645036621402383989 Application Note Y N Building and Benchmarking Deep Learning Models for Smart Sensing Appliances on MCUs 1 Chinese This application note presents the process of building and deploying deep learning models for Smart Sensing Appliances. It also highlights how to validate and evaluate the performance of a model by running it through different inference engines on an Embedded Sensing Device. 1644318754124703028011zh SSP 4.9 MB None None documents None 1644318754124703028011 /docs/zh/application-note/AN13562.pdf 4943726 /docs/zh/application-note/AN13562.pdf AN13562 documents N N 2022-02-08 Building and Benchmarking Deep Learning Models for Smart Sensing Appliances on MCUs /docs/zh/application-note/AN13562.pdf /docs/zh/application-note/AN13562.pdf Application Note N 645036621402383989 2024-07-17 pdf N zh Apr 25, 2022 645036621402383989 Application Note Y N Building and Benchmarking Deep Learning Models for Smart Sensing Appliances on MCUs false 0 APP-SW-PACK-ML-STATE-MONITOR downloads zh-Hans true 1 Y SSP 应用笔记 1 /docs/zh/application-note/AN13562.pdf 2022-02-08 1644318754124703028011zh SSP 1 Apr 25, 2022 Application Note This application note presents the process of building and deploying deep learning models for Smart Sensing Appliances. It also highlights how to validate and evaluate the performance of a model by running it through different inference engines on an Embedded Sensing Device. None /docs/zh/application-note/AN13562.pdf Chinese documents 4943726 None 645036621402383989 2024-07-17 N /docs/zh/application-note/AN13562.pdf Building and Benchmarking Deep Learning Models for Smart Sensing Appliances on MCUs /docs/zh/application-note/AN13562.pdf documents 645036621402383989 Application Note N zh None Y pdf 1 N N Building and Benchmarking Deep Learning Models for Smart Sensing Appliances on MCUs 4.9 MB AN13562 N 1644318754124703028011 true Y Softwares

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