Time-dependent gas distribution modelling
Time-dependent gas distribution modelling
复制标题
随时间变化的气体分布建模
DOI：
10.1016/j.robot.2017.05.012
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
A. Lilienthal
中科院分区：
文献类型：
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作者：
S. Asadi;Han Fan;V. Bennetts;A. Lilienthal
人工嗅觉可以帮助解决因排放有害气体而带来的紧迫环境问题。传感器网络和装有气体传感器的移动机器人可用于例如空气污染监测。在这种情况下，关键是能够从一组稀疏的测量中得出真实的气体分布模型。大多数统计气体分布模拟方法都假定气体扩散是一个时间常数的随机过程。虽然这一假设在某些情况下大致成立，但有必要模拟随时间的变化，以便能够在更广泛的现实场景中应用气体分布模拟。例如，时间不变的方法不能很好地模拟气体羽流的演变，或者由于环境条件的突然变化而导致的气体扩散的重大变化。本文提出了两种建立瓦斯分布模型的方法，这两种方法在生成瓦斯分布模型时引入了时间相关性和与时间尺度的关系，这两种方法是通过二次抽样或通过引入与测量和预测时间相关的新近权重来建立的。我们在两个真实环境中以及几个模拟实验中对这些方法进行了评估。正如预期的那样，不同分抽样策略的比较表明，只要给出足够的空间覆盖，最近的测量就能提供更多的信息，以得出当前气体分布的估计。接下来，我们比较了包含新近权重的时间相关气体分布建模方法(TD Kernel DM+V)和不考虑采样时间的最新气体分布建模方法(Kernel DM+V)。结果表明，对看不见的测量的预测有持续的改进，特别是在动态场景中。此外，本文还讨论了元参数对模型选择的影响，并比较了不同羽流条件下随时间变化的广义预测模型的性能。最后，我们研究了如何设置为其创建模型的目标时间。结果表明，当目标时间设置为测试集中的最大采样时间时，TD Kernel DM+V算法的性能最好。
Artificial olfaction can help to address pressing environmental problems due to unwanted gas emissions. Sensor networks and mobile robots equipped with gas sensors can be used for e.g. air pollution monitoring. Key in this context is the ability to derive truthful models of gas distribution from a set of sparse measurements. Most statistical gas distribution modelling methods assume that gas dispersion is a time-constant random process. While this assumption approximately holds in some situations, it is necessary to model variations over time in order to enable applications of gas distribution modelling in a wider range of realistic scenarios. Time-invariant approaches cannot model well evolving gas plumes, for example, or major changes in gas dispersion due to a sudden change of the environmental conditions. This paper presents two approaches to gas distribution modelling, which introduce a time-dependency and a relation to a time-scale in generating the gas distribution model either by sub-sampling or by introducing a recency weight that relates measurement and prediction time. We evaluated these approaches in experiments performed in two real environments as well as on several simulated experiments. As expected, the comparison of different sub-sampling strategies revealed that more recent measurements are more informative to derive an estimate of the current gas distribution as long as a sufficient spatial coverage is given. Next, we compared a time-dependent gas distribution modelling approach (TD Kernel DM+V), which includes a recency weight, to the state-of-the-art gas distribution modelling approach (Kernel DM+V), which does not consider sampling times. The results indicate a consistent improvement in the prediction of unseen measurements, particularly in dynamic scenarios. Furthermore, this paper discusses the impact of meta-parameters in model selection and compares the performance of time-dependent GDM in different plume conditions. Finally, we investigated how to set the target time for which the model is created. The results indicate that TD Kernel DM+V performs best when the target time is set to the maximum sampling time in the test set.