用微波传感器和机器学习精准识别油品，准确率达99.41%。
Machine Learning-Based Classification of Oils Using Dielectric Properties and Microwave Resonant Sensing
- 通过微波谐振传感捕获油品介电特性变化，提取特征输入模型。
- 随机森林分类器实现99.41%准确率，验证方法高效可靠。
- 非破坏性、低功耗设计，适合工业实时检测场景。
本文提出一种基于机器学习的油品分类方法，利用微波谐振传感器检测油品介电特性。油品分子组成决定其介电行为，引起传感器谐振频率与幅度响应的显著变化。这些变化被系统采集并处理，提取关键特征输入多种机器学习分类器。微波谐振传感器具有非破坏性、低功耗特点，适用于实时工业应用。通过调节油样介电常数并获取对应响应，构建了全面数据集。多个分类器在提取的谐振特征上训练评估，实验结果表明，随机森林分类器达到99.41%的分类准确率，充分展示该方法在自动化油品识别中的潜力。系统紧凑、高效且性能优异，具备在工业环境中快速可靠进行油品表征的可行性。
原文摘要 · Abstract (English)
This paper proposes a machine learning-based methodology for the classification of various oil samples based on their dielectric properties, utilizing a microwave resonant sensor. The dielectric behaviour of oils, governed by their molecular composition, induces distinct shifts in the sensor's resonant frequency and amplitude response. These variations are systematically captured and processed to extract salient features, which serve as inputs for multiple machine learning classifiers. The microwave resonant sensor operates in a non-destructive, low-power manner, making it particularly well-suited for real-time industrial applications. A comprehensive dataset is developed by varying the permittivity of oil samples and acquiring the corresponding sensor responses. Several classifiers are trained and evaluated using the extracted resonant features to assess their capability in distinguishing between oil types. Experimental results demonstrate that the proposed approach achieves a high classification accuracy of 99.41% with the random forest classifier, highlighting its strong potential for automated oil identification. The system's compact form factor, efficiency, and high performance underscore its viability for fast and reliable oil characterization in industrial environments.
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