The purpose of this study was to investigate the combustion characteristics of a direct-injection-type homogeneous charge compression ignition (HCCI) engine. From this experimental study,
we found that the diesel HCCI combustion phenomenon occurred in two stages of a combustion
pattern, which are the cool flame and the hot flame. To investigate the combustion and emission
characteristics of the HCCI engine, we evaluated the influence of intake air temperature, pressure,
and an additive on HCCI combustion and emission performance characteristics; in particular,
we focused on those characteristics of the cool and hot flame, the auto-ignition time, and the
indicated mean effective pressure (IMEP) under various engine running conditions. This research
showed that, as the intake temperature was increased and the additive was used, the onset angle
of cool and hot flames and the starting time of auto-ignition were advanced; moreover, the
influence of intake conditions (pressure, temperature) affected the cool flame and the hot flame
simultaneously, whereas the additive mainly affected the cool flame more than the hot flame. In
the higher-speed regions, the rate of the hot flame varied according to the air:fuel ratio; yet, in
the lower-speed regions, an inverse trend occurred. This result was determined based on the
time needed to reach a critical temperature for H2O2 decomposition. In the rich-mixture region,
the ignition delay was inversely proportional to the intake temperature; however, in the lean-mixture region, an inverse trend occurred. An advancement of the auto-ignition time increased
the HCCI engine output; however, excessive advancement decreased the IMEP and also increased
the NO
x
emissions, because of knocking.
In edge computing, most procedures, including data collection, data processing, and service provision, are handled at edge nodes and not in the central cloud. This decreases the processing burden on the central cloud, enabling fast responses to enddevice service requests in addition to reducing bandwidth consumption. However, edge nodes have restricted computing, storage, and energy resources to support computation-intensive tasks such as processing deep neural network (DNN) inference. In this study, we analyze the effect of models with single and multiple local exits on DNN inference in an edge-computing environment. Our test results show that a single-exit model performs better with respect to the number of local exited samples, inference accuracy, and inference latency than a multi-exit model at all exit points. These results signify that higher accuracy can be achieved with less computation when a single-exit model is adopted. In edge computing infrastructure, it is therefore more efficient to adopt a DNN model with only one or a few exit points to provide a fast and reliable inference service.
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