Jun Xu

Compared to longitudinal ventilation, there are few studies on fire source development under semi-transverse ventilation. This work studied the influence of semi-transverse ventilation on the combustion characteristics of fire sources in a scaled tunnel. The burning rate and heat transfer feedback during pool fire combustion were revealed under different longitudinal and transverse ventilation velocities. The results showed that transverse ventilation had little influence on combustion characteristics, and the burning rate was more obviously affected by longitudinal ventilation. The heat convection feedback increased monotonically with the increase of the longitudinal ventilation, which led to the increase of the total heat feedback on the fuel. The heat radiation feedback changed little, and the heat conduction feedback decreased monotonically with the increase of the longitudinal ventilation velocity. By aid of a Fire Dynamics Simulator, it was found that the flame tilted downstream and was in the flow line of the lower cold air flow coming from upstream and the upper hot smoke flow outgoing in the downstream direction. The transverse ventilation of 2 m/s or lower hardly affected the combustion field of the fire source. Therefore, semi-transverse ventilation is preferable to longitudinal ventilation from the point of view of limiting fire expansion.

The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features. Single image dehazing is one of the most important tasks in the field of image restoration. In recent years, due to the progress of deep learning, single image dehazing has made great progress. With the success of Transformer in advanced computer vision tasks, some research studies also began to apply Transformer to image dehazing tasks and obtained surprising results. However, both the deconvolution-neural-network-based dehazing algorithm and Transformer based dehazing algorithm magnify their advantages and disadvantages separately. Therefore, this paper proposes a novel Transformer–Convolution fusion dehazing network (TCFDN), which uses Transformer’s global modeling ability and convolutional neural network’s local modeling ability to improve the dehazing ability. In the Transformer–Convolution fusion dehazing network, the classic self-encoder structure is used. This paper proposes a Transformer–Convolution hybrid layer, which uses an adaptive fusion strategy to make full use of the Swin-Transformer and convolutional neural network to extract and reconstruct image features. On the basis of previous research, this layer further improves the ability of the network to remove haze. A series of contrast experiments and ablation experiments not only proved that the Transformer–Convolution fusion dehazing network proposed in this paper exceeded the more advanced dehazing algorithm, but also provided solid and powerful evidence for the basic theory on which it depends.