Xing Gao

Depression is a prevalent and severe global mental disorder, yet its diagnosis and treatment encounter numerous challenges. This study introduces an innovative depression identification network, termed DNet. Our approach utilizes facial images and local facial images as crucial sources of data. Given that facial expressions of individuals with depression at varying severity levels inherently share similar latent facial features, subtle differences exist across multiple facial regions. To achieve higher recognition accuracy, a method is required to fuse advanced semantic features between local and global features.Therefore,we proposeDNet, comprising two key components: the Feature Extraction Module (FEM) and the Vision Transformer (ViT) Block. Specifically, FEM introduces an attention mechanism that considers both channel and positional information of the feature map. Two FEMs are employed to separately process facial and local facial images, extracting critical features to generate highly semantic information-rich feature maps. Subsequently, the feature maps of both images are concatenated along the channel dimension, and the ViT Block is utilized to comprehensively learn advanced semantic features of local and global information related to different facial expression regions. Finally, a 1×1 convolution layer and a fully connected layer are applied to adjust feature channels, yielding more robust predictive results and ultimately outputting depression prediction scores.We experimentally validate the DNet network on the AVEC2014 dataset and our self-constructed CZ2023 dataset, obtaining results of MAE=6.27, RMSE=7.96, and MAE=7.46, RMSE=9.15, respectively. These results affirm the effectiveness of the proposed method.

The generation and propagation of internal waves in the ocean are a common phenomenon that plays a pivotal role in the transport of mass, momentum, and energy, as well as in global climate change. Internal waves serve as a critical component of oceanic processes, contributing to the redistribution of heat and nutrients in the ocean, which, in turn, has implications for global climate regulation. However, the automatic identification of internal waves in oceanic regions from remote sensing images has presented a significant challenge. In this research paper, we address this challenge by designing a data augmentation approach grounded in a modified deep convolutional generative adversarial network (DCGAN) to enrich MODIS remote sensing image data for the automated detection of internal waves in the ocean. Utilizing t-distributed stochastic neighbor embedding (t-SNE) technology, we demonstrate that the feature distribution of the images produced by the modified DCGAN closely resembles that of the original images. By using t-SNE dimensionality reduction technology to map high-dimensional remote sensing data into a two-dimensional space, we can better understand, visualize, and analyze the quality of data generated by the modified DCGAN. The images generated by the modified DCGAN not only expand the dataset’s size but also exhibit diverse characteristics, enhancing the model’s generalization performance. Furthermore, we have developed a deep neural network named “WaveNet,” which incorporates a channel-wise attention mechanism to effectively handle complex remote sensing images, resulting in high classification accuracy and robustness. It is important to note that this study has limitations, such as the reliance on specific remote sensing data sources and the need for further validation across various oceanic regions. These limitations are essential to consider in the broader context of oceanic research and remote sensing applications. We initially pre-train WaveNet using the EuroSAT remote sensing dataset and subsequently employ it to identify internal waves in MODIS remote sensing images. Experiments show the highest average recognition accuracy achieved is an impressive 98.625%. When compared to traditional data augmentation training sets, utilizing the training set generated by the modified DCGAN leads to a 5.437% enhancement in WaveNet’s recognition rate.