In this work, the characteristics of R1234yf, R1234ze, R134a on reefer container were studied. According to pressure enthalpy (p-h) diagram, a computer code developed by Matlab was used to calculate power consumption, coefficient of performance (COP), compression ratio, discharge temperature, etc. The result shows that, at the same degree of subcooling and superheat, the power consumption, discharge temperature and compression ratio of R1234yf is higher than that of R1234ze, R134a. The COP of R1234yf is maximum. R1234yf can be used as an environment-friendly replacement for R134a in reefer container.
With the rapid development of automobile intelligent and networking, substantial information is exchanged between in-vehicle network system and the outside world, thereby threatening the automobile security. Intrusion detection is an important technology to realize the security of in-vehicle networks. The existing research on in-vehicle network intrusion detection mainly focuses on the improvement of detection accuracy, but it lacks consideration of timeliness, whereas the in-vehicle network is a time-sensitive system. This study proposes an anomaly detection method for in-vehicle Controller Area Network (CAN) based on lightweight neural network to reduce the operation time while maintaining the detection accuracy. The redundant neuron screening method and model compression algorithm for layer-by-layer neuron pruning are designed. This presented method can delete the neurons with small contribution and obtain lightweight neural network model. The detection performance of model compression and noncompression is compared through experiments. Results show that under the two real in-vehicle datasets, the detection time is accelerated by 47.7 times and 34.2 times at most, and the average accuracy is increased by 14.5% and 15.7%.
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