Existing infrared and visible image fusion models ignore illumination factors in fusion process and use conventional fusion strategies， leading to the fusion results with the loss of detail information and inconspicuous salient information. To solve these problems， a deep learning model for infrared and visible image fusion based on illumination weight allocation and attention was proposed. Firstly， an Illumination Weight Allocation Network （IWA-Net） was designed to estimate the illumination distribution and calculate illumination weights. Secondly， a CM-L1-norm fusion strategy was introduced to enhance the dependency between pixels and achieve smooth processing of salient features. Finally， a decoding network composed of fully convolutional layers was employed to reconstruct fused images. The results of the fusion experiments on publicly available datasets show that the proposed model outperforms the contrastive models， with improvements observed in all six selected evaluation metrics； specifically， the Spatial Frequency （SF） and Mutual Information （MI） metrics increase by 45% and 41% in average， respectively. The proposed model effectively reduces edge blurring and enhances clarity and contrast of the fused images. The fusion results of the proposed model exhibits superior performance in both subjective and objective aspects compared to other contrastive models.

To address the issue of siting for Electric EVCS （Vehicle Charging Station）， an urban charging station siting method based on spatial semantics and individual activities was proposed. First， according to the urban planning， unsupervised learning was used to cluster the Point Of Interests （POIs） out of the service radius to determine the number of new charging stations. Then， Constrained Two-Archive Evolutionary Algorithm （CTAEA） was used to solve the objective function to optimize the electric vehicle siting scheme under the constraints of maximizing the distance between stations and covering the most POIs with new charging stations. The trajectory data and POIs of taxis in the second-ring road of Chengdu were used as the experimental samples， and siting scheme with 15 charging stations was planned. Experimental results show that compared with NSGA2 （Non-dominated Sorting Genetic Algorithm 2） and SPEA2 （Strength Pareto Evolutionary Algorithm 2）， CTAEA improves 22.9 and 20.6 percentage points on POI coverage， and reduces 18.9% and 25.5% on driver’s average selected distance， which illustrates the convenience and rationality of the method in electric vehicle charging station siting.

Focused on the issue that the current large-scale networks are not suitable to be applied on resource-starved mobile devices like smart phones and tablet computers， and the pooling layer will lead to the sparsity of feature map， which ultimately affect the recognition accuracy of the neural network， a lightweight face recognition neural network namely ShuffaceNet was proposed， a smooth nonlinear Log-Mean-Exp function ThetaMEX was designed， and an end-to-end trainable ThetaMEX Global Pool Layer （TGPL） was proposed， so as to reduce network parameters and improve computing speed while ensuring the accuracy of the algorithm， achieving the purpose that the network can be effectively deployed on mobile devices with limited resources. ShuffaceNet has about 3 600 parameters， and the model size is only 3.5 MB. The recognition test results on LFW （Labled Faces in the Wild）， AgeDB-30 （Age Database-30） and CFP （Celebrities in Frontal Profile） face datasets show that the accuracy of ShuffaceNet reaches 99.32%， 93.17%， 94.51% respectively. Compared with the traditional networks such as MobileNetV1， SqueezeNet and Xception， the proposed network has the size reduced by 73.1%， 82.1% and 78.5% respectively， and the accuracy on AgeDB-30 dataset improved by 5.0%， 6.3% and 6.7% respectively. It can be seen that the proposed network based on ThetaMEX global pooling can improve the model accuracy.