The results of collapsed building detection act as an important reference for damage assessment after an earthquake, which is crucial for governments in order to efficiently determine the affected area and execute emergency rescue. For this task, unmanned aerial vehicle (UAV) images are often used as the data sources due to the advantages of high flexibility regarding data acquisition time and flying requirements and high resolution. However, collapsed buildings are typically distributed in both connected and independent pieces and with arbitrary shapes, and these are generally more obvious in the UAV images with high resolution; therefore, the corresponding detection is restricted by using conventional convolutional neural networks (CNN) and the detection results are difficult to evaluate. In this work, based on faster region-based convolutional neural network (Faster R-CNN), deformable convolution was used to improve the adaptability to the arbitrarily shaped collapsed buildings. In addition, inspired by the idea of pixelwise semantic segmentation, in contrast to the intersection over union (IoU), a new method which estimates the intersected proportion of objects (IPO) is proposed to describe the degree of the intersection of bounding boxes, leading to two improvements: first, the traditional non-maximum suppression (NMS) algorithm is improved by integration with the IPO to effectively suppress the redundant bounding boxes; second, the IPO is utilized as a new indicator to determine positive and negative bounding boxes, and is introduced as a new strategy for precision and recall estimation, which can be considered a more reasonable measurement of the degree of similarity between the detected bounding boxes and ground truth bounding boxes. Experiments show that compared with other models, our work can obtain better precision and recall for detecting collapsed buildings for which an F1 score of 0.787 was achieved, and the evaluation results from the suggested IPO are qualitatively closer to the ground truth. In conclusion, the improved NMS with the IPO and Faster R-CNN in this paper is feasible and efficient for the detection of collapsed buildings in UAV images, and the suggested IPO strategy is more suitable for the corresponding detection result’s evaluation.

Virtual prototyping of power electronic modules aims to allow rapid evaluation of potential designs without building and testing physical prototypes. Among the interests in thermal models of the virtual modules, process of compact thermal models needs effective methodology to fast generate small models describing the thermal performance of a potential design. This study chooses the Generalized Minimized Residual (GMRES) Algorithm to process thermal models due to its efficiency. Based on that, a machine learning aided surrogate model is proposed for the prediction of thermal performance since existing approaches take much time to determine the thermal response to a particular input power. This surrogate model is created by training a dedicated artificial neural network (ANN) on simulation data, after that this model can quickly map the module temperature and the power input in time domain. In the training process, cross-validation method is introduced to determine which neuron structure should be selected for the practical data generated by thermal equations. The test group is noted in cross-validation to give the prediction performance of structure candidates. To verify the proposed method, the resulting data of trained surrogate models are compared with the accurate simulation data after the ANN based cross-validation.