In interactive point cloud segmentation, users can achieve higher accuracy object masks than in instance segmentation by performing limited positive and/or negative clicks on the objects of interest in the scene. Existing methods often employ sparse click representations, leading the model to focus more on local detail features around the click points and failing to fully exploit the guidance information provided by each click, thus impacting the click effectiveness. We utilize a dense representation that reflects spatial distance relationships, known as the distance map, as the click channel to tackle the sparsity problem of click representation in current approaches. Based on the distance map, we introduce ClickEnhance, which is designed to maximize the guiding impact of each click. The proposed method encompasses the design of a click-specific encoder and the utilization of contrastive learning. The Click-Specific Encoder ensures that the network can adequately consider the influence of individual clicks during the feature encoding phase. Contrastive learning, on the other hand, reduces the feature distance between the click points and the target object, thus simplifying the subsequent segmentation process. Experimental results demonstrate that the ClickEnhance method markedly improves segmentation performance across multiple datasets, exhibiting superior generalization capabilities on challenging datasets compared to the state-of-the-art methods. This allows for the generation of high-precision object-level masks with fewer interactions, indicating great potential for practical applications.

For the sake of the performance improvement of soft-switching inverters, the paper designs an efficient resonant pole inverter. Main switches in the designed inverter can attain zero-voltage switching with the low peak voltage and current stress, which is conducive to the low loss of main switches. Moreover, the striking feature of the designed inverter different from existing resonant pole inverters is that two sets of independent symmetrical auxiliary circuits on each bridge arm are not jointly involved in the operation in a switching cycle, and that they are alternately involved in the operation based on the different direction of load current, not only providing the condition of zero-voltage switching for main switches but also improving the reliability of soft-switching of main switches in the case of open failure of any auxiliary device. The paper provides the detailed interpretation on each working status during a switching cycle. Experimental waveforms manifest that main switches can attain zero-voltage switching. The efficiency test at rated operation state also manifests that the efficiency of the designed inverter is up to 99.1% without any failure of auxiliary devices and 98.2% with open failure of an auxiliary switch on each bridge arm, confirming that the designed inverter has superiority over the comparison object in the field of efficiency even if open failure of an auxiliary switch on each bridge arm occurs in both of them. Hence, the alternate operation of symmetrical double auxiliary circuits based on different direction of load current in the designed inverter is especially beneficial to reliability improvement of soft-switching with open failure of any auxiliary device.

Deep convolutional neural network can effectively extract hidden patterns in images and learn realistic image priors from the training set. And fully convolutional networks (FCNs) have achieved state‐of‐the‐art performance in the image segmentation. However, these methods have the disadvantages of noise, boundary roughness and no prior shape. Therefore, this study proposes a level set with the deep prior method for the image segmentation based on the priors learned by FCNs. The FCNs can learn high‐level semantic patterns from the training set. Also, the output of the FCNs represents the high‐level semantic information as a probability map and the global affine transformation can obtain the optimal affine transformation of the intrinsic prior shape. Moreover, the improved level set method integrates the information of the original image, the probability map and the corrected prior shape to achieve the image segmentation. Compared with the traditional level set method of simple scenes, the proposed method solves the disadvantage of FCNs by using the high‐level semantic information to segment images of complex scenes. Finally, Portrait data set are used to verify the effectiveness of the proposed method. The experimental results show that the proposed method can obtain more accurate segmentation results than the traditional FCNs.

Fully convolutional neural network (FCN) has achieved state-of-the-art performance in the task of time series classification without any heavy preprocessing. However, the FCN cannot effectively capture features of different frequencies. Therefore, this paper proposed a novel FCN structure based on the multi-frequency decomposition (MFD) method. In order to extract more features of different frequencies, the MFD based on real fast Fourier transform (RFFT) is set as a layer of the FCN to decompose the original signal into n sub-signals of different frequency bands. And then the improved FCN fuse those features of different frequencies together to obtain time series classification. Finally, compared with the existing state-of-the-art methods, the proposed method is effectively verified through some datasets in UCR Time Series Classification archive.