Abstract On-road emission remote sensing (OERS) is an ideal means to identify the on-road high-emitting vehicles, which can scan thousands of vehicles within a day without interfering the normal driving. Due to the complex and varying measuring environments and vehicular operating states, it is reasonable to determine the high-emitters not only by the OERS-output pollutant concentration, but also the other information, such as meteorological and vehicular conditions. This paper aims to establish a high-emitter identification model by machine learning technologies to combine the OERS outputs and periodic emission inspection results. The periodic emission inspection, which is conducted in vehicular inspection stations (VIS), is relatively accurate since the measuring environments and vehicular operating states are controllable, and thereby the periodic emission inspection results are considered as the truth values (or labels). However, VIS is extremely inefficient compared with OERS, thus resulting in scarce labels. Moreover, due to some practical issues, such as staff cheating, only the positive labels (high-emitters) are reliable. Therefore, this paper studies the possibility of employing the one-class classification and graph-based label propagation to solve the problem of scarce positive labels. The experimental results show that the high-emitter identification model based on one-class classification can achieve satisfactory performance, which could be further improved by the application of graph-based label propagation.

中文翻译：

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基于稀缺正标签的道路排放遥感高排放车辆识别

摘要 道路排放遥感（OERS）是一种理想的道路高排放车辆识别手段，它可以在不干扰正常驾驶的情况下一天内扫描数千辆车辆。由于测量环境和车辆运行状态复杂多变，因此不仅可以通过 OERS 输出污染物浓度来确定高排放物，还可以通过气象和车辆条件等其他信息来确定高排放物。本文旨在通过机器学习技术建立高排放识别模型，将 OERS 输出和定期排放检测结果相结合。在车辆检测站（VIS）进行的定期排放检测相对准确，因为测量环境和车辆运行状态是可控的，从而将定期排放检测结果视为真值（或标签）。然而，与 OERS 相比，VIS 效率极低，从而导致标签稀缺。此外，由于一些实际问题，例如员工作弊，只有正面标签（高发射器）是可靠的。因此，本文研究了采用一类分类和基于图的标签传播来解决正标签稀缺问题的可能性。实验结果表明，基于一类分类的高发射器识别模型可以获得令人满意的性能，可以通过基于图的标签传播的应用进一步改进。从而导致标签稀缺。此外，由于一些实际问题，例如员工作弊，只有正面标签（高发射器）是可靠的。因此，本文研究了采用一类分类和基于图的标签传播来解决正标签稀缺问题的可能性。实验结果表明，基于一类分类的高发射器识别模型可以获得令人满意的性能，可以通过基于图的标签传播的应用进一步改进。从而导致标签稀缺。此外，由于一些实际问题，例如员工作弊，只有正面标签（高发射器）是可靠的。因此，本文研究了采用一类分类和基于图的标签传播来解决正标签稀缺问题的可能性。实验结果表明，基于一类分类的高发射器识别模型可以获得令人满意的性能，可以通过基于图的标签传播的应用进一步改进。本文研究了采用一类分类和基于图的标签传播来解决正标签稀缺问题的可能性。实验结果表明，基于一类分类的高发射器识别模型可以获得令人满意的性能，可以通过基于图的标签传播的应用进一步改进。本文研究了采用一类分类和基于图的标签传播来解决正标签稀缺问题的可能性。实验结果表明，基于一类分类的高发射器识别模型可以获得令人满意的性能，可以通过基于图的标签传播的应用进一步改进。