一种基于pla的光致变色材料和机器学习模型的紫外测量新方法

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Eşref Erdoğan;Şekip Esat Hayber;Ömer Galip Saraçoğlu
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引用次数: 0

摘要

本文提出了一种利用手机相机精确测量紫外(UV)光致变色材料(PLA-PM)颜色变化与紫外量之间关系的新方法。使用手机相机记录PLA-PM颜色变化的视频。获得的数据用于机器学习,并使用机器学习模型预测引起颜色变化的紫外光量。模型中的目标变量是通过Arduino从UV传感器读取的电压值。数据集准备通过6个图像处理阶段处理不同的高分辨率和帧数/秒图像。图像处理阶段以对最终图像的三色空间通道的值进行归一化结束。这项工作用人工智能技术增强了传统的比色测量,在紫外线指数测量、医疗测量和紫外线敏感纺织品等研究中展示了潜在的适用性。实验表明,用手机测量紫外线的可行性,而不依赖于昂贵的设备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Method for UV Measurement Using PLA-Based Photochromic Material and Machine Learning Models
A new method has been developed that can accurately measure the relationship between color change under ultraviolet (UV) light and the amount of UV using a mobile phone camera to observe the color changes of a polylactic acid-based photochromic material (PLA-PM). The videos of the color change of the PLA-PM were recorded using a mobile phone camera. The obtained data were used for machine learning, and the amount of UV light causing the color change was predicted using machine learning models. The target variable in the models is the voltage values read from the UV sensor via Arduino. Dataset preparations process different high-resolution and frames/s images through six image processing stages. Image processing stages end with the normalization of values for the final image’s three-color space channels. This work, which enhances traditional colorimetric measurements with artificial intelligence technologies, demonstrates potential applicability in studies, such as UV index measurement, medical measurements, and UV-sensitive textile products. The study experimentally shows the feasibility of measuring UV with mobile phones without relying on expensive devices.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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