Hydrogen is the most promising alternative fuel in the field of engines. Exhaust heat-assisted methanol dissociation is an attractive approach for generating hydrogen. In this work, simulations are conducted on a compression ignition engine fueled with different proportions of diesel-dissociated methanol gas (DMG) blends at intermediate engine speed, full load, and 0% EGR ratio. The results reveal that the indicated thermal efficiency and indicated mean effective pressure are greatly enhanced, combustion efficiency is increased, and regular emissions of CO, HC, and soot are reduced, while NOx emissions are reduced with increased DMG substitution. In addition, a simulation is conducted at an intermediate engine speed, full load, 15% DMG substitution ratio, and varying EGR ratios of 0–20%. The results indicate that the dual-fuel engine outperforms the original engine with respect to power, fuel economy, and regular emissions, once an optimal EGR rate is adopted.

The swift and precise detection of vehicles plays a significant role in intelligent transportation systems. Current vehicle detection algorithms encounter challenges of high computational complexity, low detection rate, and limited feasibility on mobile devices. To address these issues, this paper proposes a lightweight vehicle detection algorithm based on YOLOv7-tiny (You Only Look Once version seven) called Ghost-YOLOv7. The width of model is scaled to 0.5 and the standard convolution of the backbone network is replaced with Ghost convolution to achieve a lighter network and improve the detection speed; then a self-designed Ghost bi-directional feature pyramid network (Ghost-BiFPN) is embedded into the neck network to enhance feature extraction capability of the algorithm and enriches semantic information; and a Ghost Decouoled Head (GDH) is employed for accurate prediction of vehicle location and species; finally, a coordinate attention mechanism is introduced into the output layer to suppress environmental interference. The WIoU loss function is employed to further enhance the detection accuracy. Ablation experiments results on the PASCAL VOC dataset demonstrate that Ghost-YOLOv7 outperforms the original YOLOv7-tiny model. It achieving a 29.8% reduction in computation, 37.3% reduction in the number of parameters, 35.1% reduction in model weights, 1.1% higher mean average precision (mAP), the detection speed is higher 27FPS compared with the original algorithm. Ghost-YOLOv7 was also compared on KITTI and BIT-vehicle datasets as well, and the results show that this algorithm has the overall best performance.