一个基于深度学习的实时低温和高温监测系统,采用简单的身体传感器
Egemen Nazife Yazlik1,2, Omer Galip Saracoglu3
1Department of Electrical and Energy, Nevsehir Haci Bektas Veli University, Nevsehir, Merkez, Turkiye.
概括
使用3D打印的热色材料和卷积神经网络 (CNN) 的新型身体传感器准确监测低温和高温. 该系统实现了96.1%的验证准确度,用于实时温度传感.
科学领域:
- 生物医学工程 生物医学工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 准确的实时监测体温对于检测低温和高温至关重要.
- 传统的温度监测方法在某些应用中可能存在局限性.
- 热色材料提供视觉温度指示,但需要强大的解释方法.
研究的目的:
- 开发一个实时低温和高温监测系统.
- 利用基于3D打印的热色材料的简单身体传感器.
- 使用卷积神经网络 (CNN) 进行准确的温度分析.
主要方法:
- 使用3D打印的热色聚乳酸 (PLA) 材料制造了一个身体传感器.
- 亚历克斯网CNN模型使用转移学习进行了微调.
- 一个包含510张图像的数据集,每个温度类 (28-44°C) 包含30张图像,用于培训 (80%) 和验证 (20%).
主要成果:
- 该系统使用微调的AlexNet CNN实现了96.1%的验证准确率.
- 3D打印的热色传感器证明了有效的视觉温度监测.
- 开发的系统提供可靠的实时温度数据.
结论:
- 与微调的AlexNet CNN集成的3D打印热色传感器是实时低温和高温监测的可行解决方案.
- 该系统的高精度和简单的传感器设计表明它有可能被广泛使用.
- 该技术适用于需要视觉和自动监控的微妙温度传感应用.
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