Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

AIPI: Network Status Identification on Multi-Protocol Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2025
Same author

Evaluating and Enhancing Face Anti-Spoofing Algorithms for Light Makeup: A General Detection Approach.

Sensors (Basel, Switzerland)·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Sep 18, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K

基于RFID的实时盐度监测,使用自适应EKF.

Renhai Feng1,2,3, Xinyi Lin1

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的RFID无线传感系统,用于实时,非侵入性的盐度监测. 该系统使用自适应扩展卡尔曼波器 (AEKF) 进行工业和废水应用中的准确和高效测量.

关键词:
科尔科尔模型适应式扩展卡尔曼波器 (AEKF)度监测 度监测 度监测 度监测 度监测无线电频率识别 (RFID) 是一种技术.

更多相关视频

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.8K
A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants
08:13

A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

Published on: February 19, 2016

9.5K

相关实验视频

Last Updated: Sep 18, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.8K
A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants
08:13

A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

Published on: February 19, 2016

9.5K

科学领域:

  • 电气工程 电气工程
  • 化学工程是化学工程的重要组成部分.
  • 环境监测 环境监测

背景情况:

  • 精确的盐度监测对于工业过程和废水管理至关重要.
  • 现有的方法往往缺乏实时能力或需要侵入性采样,造成局限性.

研究的目的:

  • 开发一种新的无线射频识别 (RFID) 无线传感系统,用于非侵入性,实时的溶液度监测.
  • 将物理建模与先进的估计算法集成在一起,以提高测量精度和效率.

主要方法:

  • 结合了科尔-科尔和裂圆柱体电容器 (SCC) 模型,以创建基于物理的状态空间模型.
  • 模拟度动态作为隐藏的马尔科夫过程,并使用自适应扩展卡尔曼波器 (AEKF) 追踪.
  • AEKF算法自动调整噪声共变矩阵,避免复杂的反转.

主要成果:

  • 在CaCl2溶液 (2-10 g/L) 中,平均相对误差 (MRE) 为2.8%.
  • 保持MRE低于3%的最佳范围 (2-8 g/L CaCl2) 即使带入噪声,也显示出强度.
  • 与基线方法相比,AEKF算法显示精度和计算效率有所提高.

结论:

  • 拟议的RFID无线传感系统为非侵入性盐度监测提供了强大而高效的解决方案.
  • AEKF算法提供了一种计算效率高的方法,用于实时跟踪度动态.
  • 这项技术在工业过程控制和废水管理方面的应用潜力很大.