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首页> 外文会议>Conference on sensing technologies for global health, military medicine, disaster response, and environmental monitoring >Automated classification of single airborne particles from two-dimension, angle-resolved optical scattering (TAOS) patterns
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Automated classification of single airborne particles from two-dimension, angle-resolved optical scattering (TAOS) patterns

机译：从两维，角度分辨光学散射（Taos）模式自动分类单个空气颗粒

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摘要

Two-dimension, angle-resolved optical scattering (TAOS) is an experimental technique by which patterns of LASER light intensity scattered by single (micrometer or sub-micrometer sized) airborne particles are collected. In the past 10 years TAOS instrumentation has evolved from laboratory prototypes to field-deployable equipment; patterns are collected by the thousands during indoor or outdoor sampling in short times. Although comparison between experimental and computed scattering patterns has been carried out extensively, there is no satisfactory way to relate a given pattern to the particle it comes from. This paper reports about the ongoing development and implementation of a method which is aimed at classifying patterns, rather than identifying original particles. A machine learning algorithm includes the extraction of morphological features and their multivariate statistical analysis. A classifier is trained and validated in a supervised mode, by relying on patterns from known materials. Then the tuned classifier is applied to the recognition of patterns of unknown origin.
机译：两个尺寸，角度分辨光学散射（TaOS）是一种实验技术，通过单个（千分尺或亚微米尺寸）空气传播颗粒散射的激光强度模式。在过去的10年里，Taos仪器从实验室原型进化到现场部署的设备;在短时间内，在室内或室外采样期间收集图案。尽管实验和计算的散射模式之间的比较已经广泛进行，但是没有令人满意的方式将给定的图案与其来自的粒子相关联。本文报告了持续的开发和实施，该方法旨在进行分类模式，而不是识别原始颗粒。机器学习算法包括提取形态特征及其多变量统计分析。通过依赖来自已知材料的模式，在监督模式下培训并验证分类器。然后将调谐的分类器应用于识别未知原点的模式。

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