In Vapor Cycle Systems, the mass flow sensor playsa key role for different
monitoring and control purposes. However,physical sensors can be inaccurate,
heavy, cumbersome, expensive orhighly sensitive to vibrations, which is
especially problematic whenembedded into an aircraft. The conception of a
virtual sensor, basedon other standard sensors, is a good alternative. This
paper has twomain objectives. Firstly, a data-driven model using a
ConvolutionalNeural Network is proposed to estimate the mass flow of
thecompressor. We show that it significantly outperforms the standardPolynomial
Regression model (thermodynamic maps), in terms of thestandard MSE metric and
Engineer Performance metrics. Secondly,a semi-automatic segmentation method is
proposed to compute theEngineer Performance metrics for real datasets, as the
standard MSEmetric may pose risks in analyzing the dynamic behavior of
VaporCycle Systems.

本文提出了一个数据驱动模型，使用卷积神经网络来估计压缩机的质量流量，并展示了它在标准均方误差和工程师性能指标方面明显优于标准多项式回归模型。此外，还提出了一种半自动分割方法来计算真实数据集的工程师性能指标，因为标准均方误差指标在分析蒸汽循环系统的动态行为时可能存在风险。

基于卷积神经网络的工业飞机蒸气循环系统压缩机质量流量估计器

CNN-based Compressor Mass Flow Estimator in Industrial Aircraft Vapor  Cycle System

Conventional tracking paradigm takes in instantaneous measurements such as
range and bearing, and produces object tracks across time. In applications such
as autonomous driving, lidar measurements in the form of point clouds are
usually passed through a "virtual sensor" realized by a deep learning model, to
produce "measurements" such as bounding boxes, which are in turn ingested by a
tracking module to produce object tracks. Very often multiple lidar sweeps are
accumulated in a buffer to merge and become the input to the virtual sensor. We
argue in this paper that such an input already contains temporal information,
and therefore the virtual sensor output should also contain temporal
information, not just instantaneous values for the time corresponding to the
end of the buffer. In particular, we present the deep learning model called
MULti-Sweep PAired Detector (MULSPAD) that produces, for each detected object,
a pair of bounding boxes at both the end time and the beginning time of the
input buffer. This is achieved with fairly straightforward changes in commonly
used lidar detection models, and with only marginal extra processing, but the
resulting symmetry is satisfying. Such paired detections make it possible not
only to construct rudimentary trackers fairly easily, but also to construct
more sophisticated trackers that can exploit the extra information conveyed by
the pair and be robust to choices of motion models and object birth/death
models. We have conducted preliminary training and experimentation using Waymo
Open Dataset, which shows the efficacy of our proposed method.

该研究论文提出了 MULti-Sweep PAired Detector（MULSPAD），它通过一种深度学习模型在输入缓冲的结束时间和开始时间分别为每个检测到的物体生成一对边界框。而这种成对的检测不仅可以很容易地构建简单的跟踪器，还可以构建更复杂的跟踪器，可以利用成对信息，对运动模型和物体生成 / 死亡模型的选择具有鲁棒性。我们使用 Waymo Open Dataset 进行了初步的训练和实验，结果证明了我们提出方法的有效性。