16 research outputs found
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The Effect of Diluent Gases In The Shock Tube and Rapid Compression Machine
Studying the details of hydrocarbon chemistry in an internal combustion engine is not straightforward. A number of factors, including varying conditions of temperature and pressure, complex fluid motions, as well as variation in the composition of gasoline, render a meaningful characterization of the combusting system difficult. Some simplified experimental laboratory devices offer an alternative to complex engine environments: they remove some of the complexities that exist in real engines but retain the ability to work under engine-relevant conditions. The choice of simplified experimental devices is limited by the range of temperature and pressure at which they can operate; only the shock tube and rapid compression machine (RCM) can reach engine-relevant temperatures and pressures quickly enough and yet withstand the high pressures that occur after the ignition event. Both devices, however, suffer a common drawback: the use of inert diluent gases has been shown to affect the measured ignition delay time under some experimental conditions. Interestingly, this effect appears to be opposite in the shock tube and RCM: in the comparative study of the carrier gases argon and nitrogen, argon decreases the ignition delay time in the shock tube, but increases it in the RCM. This observation is investigated in more detail in this study
Dense Feature Aggregation and Pruning for RGBT Tracking
How to perform effective information fusion of different modalities is a core
factor in boosting the performance of RGBT tracking. This paper presents a
novel deep fusion algorithm based on the representations from an end-to-end
trained convolutional neural network. To deploy the complementarity of features
of all layers, we propose a recursive strategy to densely aggregate these
features that yield robust representations of target objects in each modality.
In different modalities, we propose to prune the densely aggregated features of
all modalities in a collaborative way. In a specific, we employ the operations
of global average pooling and weighted random selection to perform channel
scoring and selection, which could remove redundant and noisy features to
achieve more robust feature representation. Experimental results on two RGBT
tracking benchmark datasets suggest that our tracker achieves clear
state-of-the-art against other RGB and RGBT tracking methods.Comment: arXiv admin note: text overlap with arXiv:1811.0985