In this paper, we present a novel approach that subtly combines the transformer with grasping CNN to achieve more optimal grasps in complex real-life situations. The approach comprises two unique designs that effectively improve grasp precision in complex scenes. The first essential design uses self-attention mechanisms to capture contextual information from RGB images, boosting contrast between key object features and their surroundings. We precisely adjust internal parameters to balance accuracy and computing costs. The second crucial design involves building a feature fusion bridge that processes all one-dimensional sequence features at once to create an intuitive visual perception for the detection stage, ensuring a seamless combination of the transformer block and CNN. These designs eliminate noise features in complex backgrounds and emphasize graspable object features, providing valuable semantic data to the subsequent grasping CNN to achieve appropriate grasping. We evaluated the approach on the Cornell and VMRD datasets. According to the experimental results, our method achieves better performance than the original grasping CNN in single-object and multi-object scenarios, exhibiting 97.7% and 72.2% accuracy on the Cornell and VMRD grasp datasets using RGB, respectively.

In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4–9% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is −6 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%.

Multimodal sentiment analysis aims to understand people’s attitudes and opinions from different data forms. Traditional modality fusion methods for multimodal sentiment analysis con-catenate or multiply various modalities without fully utilizing context information and the correlation between modalities. To solve this problem, this article provides a new model based on a multimodal sentiment analysis framework based on a recurrent neural network with a complex attention mechanism. First, after the raw data is preprocessed, the numerical feature representation is obtained using feature extraction. Next, the numerical features are input into the recurrent neural network, and the output results are multimodally fused using a complex attention mechanism layer. The objective of the complex attention mechanism is to leverage enhanced non-linearity to more effectively capture the inter-modal correlations, thereby improving the performance of multimodal sentiment analysis. Finally, the processed results are fed into the classification layer and the sentiment output is obtained using the classification layer. This process can effectively capture the semantic information and contextual relationship of the input sequence and fuse different pieces of modal information. Our model was tested on the CMU-MOSEI datasets, achieving an accuracy of 82.04%.

Lane detection, a crucial component of autonomous driving systems, is in charge of precise lane location to ensure that cars navigate lanes appropriately. However, in challenging conditions like shadows and extreme lighting, lanes may become obstructed or blurred, posing a significant challenge to the lane-detection task as the model struggles to extract sufficient visual information from the image. The current anchor-based lane-detection network detects lanes in complex scenes by mapping anchors to images to extract features and calculating the relationship between each anchor and other anchors for feature fusion. However, it is insufficient for anchors to extract subtle features from images, and there is no guarantee that the information carried by each anchor is valid. Therefore, this study proposes the adaptive cross-scale ROI fusion network (ACSNet) to fully extract the features in the image so that the anchor carries more useful information. ACSNet selects important anchors in an adaptive manner and fuses these important anchors with the original anchors across scales. Through this feature extraction method, the features of different field-of-view ranges under complex road surfaces can be learned, and diversified features can be integrated to ensure that lanes can be well detected under complex road surfaces such as shadows and extreme lighting. Furthermore, due to the slender structure of lane lines, there are relatively few useful features in the images. Therefore, this study also proposes a Three-dimensional Coordinate Attention Mechanism (TDCA) to enhance image features. The Three-dimensional Coordinate Attention Mechanism extensively explores relationships among features in the row, column, and spatial dimensions. It calculates feature weights for each of these dimensions and ultimately performs element-wise multiplication with the entire feature map. Experimental results demonstrate that our network achieves excellent performance on the existing public datasets, CULane and Tusimple.

Location information on Twitter plays a critical role in emergency detection, event recommendation, and disaster warning. However, only a limited amount of Twitter data are geotagged. Previous research has presented various models for inferring location based on text, social relations, and contextual data, yielding highly promising results. Nonetheless, these existing methods have certain limitations that need to be addressed. Firstly, most of the existing methods overlook the role of local celebrities (well-known users in the local community) as indicators of location within the social network. Secondly, they fail to consider the associations between words in tweets, resulting in insufficiently rich features extracted from the tweets. We propose a multi-view-based location inference model called MVGeo to overcome these limitations. In the network view, our approach employs the Gaussian Mixture Model (GMM) to identify and retain local celebrities, thereby strengthening user location associations. In the tweet view, we construct a heterogeneous graph based on the co-occurrence relationship between words in tweets and the user’s mentioned relationship with the words. This allows us to fully leverage the local correlation between words and the global correlation to extract tweet features more comprehensively. Finally, we employ a modified multi-layer graph convolutional network, called Gate-GCN, to fuse the network and tweet information. This expansion of the feature space enables us to extract sample features from multiple perspectives. To demonstrate the effectiveness of MVGeo, we conduct exhaustive experimental evaluations on a publicly available dataset and compare its performance against several state-of-the-art benchmark models. The results confirm the superior performance of the proposed model.